Physiological signal denoising

ABSTRACT

Physiological signals are denoised. In accordance with an example embodiment, a denoised physiological signal is generated from an input signal including a desired physiological signal and noise. The input signal is decomposed from a first domain into subcomponents in a second domain of higher dimension than the first domain. Target subcomponents of the input signal that are associated with the desired physiological signal are identified, based upon the spatial distribution of the subcomponents. A denoised physiological signal is constructed in the first domain from at least one of the identified target subcomponents.

RELATED PATENT DOCUMENTS

This patent document is a continuation under 35 U.S.C. §120 of U.S.patent application Ser. No. 12/938,995 filed on Nov. 3, 2010 (U.S. Pat.No. 8,632,465), which claims the benefit under 35 U.S.C. §119 of U.S.Provisional Patent Application Ser. No. 61/257,718 filed on Nov. 3,2009, and of U.S. Provisional Patent Application Ser. No. 61/366,052filed on Jul. 20, 2010; each of these patent documents is fullyincorporated herein by reference.

FIELD OF INVENTION

Various aspects of the present invention relate to the processing ofphysiological signals, and more particular aspects relate to removingnoise and extracting and compressing information.

BACKGROUND

Implantable and external devices are used to monitor physiologic signalsof human and animal subjects. These devices may incorporate varioustypes of sensors and can measure and record signals from those sensorsfor processing by a system or monitoring center located remote from thesubject, or in other cases, the device may perform some or all of thedesired signal processing and forward the resulting information to aremote system for display, recording, or further processing.

The signal processing is performed to extract information from thesignal in order to assess the physiological condition of the monitoredsubject and often to evaluate the response of the subject to a therapyor experimental protocol. For example, devices that measure ECG andblood pressure are routinely used to assess cardiovascular function.Clinicians can use this information to make therapy decisions andresearchers can use this information to assess the safety and utility ofexperimental therapies. This information is also used for closed loopcontrol of therapy delivery. In other examples, measurements ofperipheral nerve activity (PNA), respiration, blood oxygen, bloodglucose, EEG, EMG, heart sounds and blood flow signals are processed toextract information for clinical or research purposes.

There is an increasing reliance on automatic processing to extractinformation in order to reduce labor and costs and to more consistentlyand accurately evaluate the condition of the subject. In therapeuticdevices automatic extraction of this information is often essential forfeedback control. However, accurate automatic extraction of informationis often challenging or is compromised by the presence of noise.

In some physiologic signal processing applications, automated analysisis complicated by the fact that measured signals are the result ofactivity of multiple sources, referred to as multi-source signals. Anexample of a multi-source signal is ECG measured on the surface of thebody where electrical activity is sensed from both the atria andventricles as well as skeletal muscles. It is useful, for example, toobserve atrial activity independent of ventricular activity in order toimprove the detection of atrial arrhythmias. Current techniques forproviding signal source extraction of multi-source signals, such asindependent component analysis (ICA), assume independence of sources andperformance is compromised when this assumption is invalid, such as isthe case when separating atrial and ventricular activity in an ECG. Inaddition, ECGs are often recorded from ambulatory subjects using a smallnumber of sensing leads, further complicating signal source extractiondue to the mixing of sources inherent in a small number of leads.

Other signals, such as peripheral nerve activity (PNA) and brainstemauditory response, have proven difficult to analyze because of very lowsignal-to-noise ratio (SNR). Visual analysis of these signals is ofteninadequate to detect important features and obtain a quantitativeevaluation.

Many physiological signal processing techniques have been difficult tosuccessfully implement under certain conditions, particularly whenprocessing signals from ambulatory subjects where the signals are oftenquite noisy. For example, measurements of ECG parameters such as heartrate, QT interval, PR interval, as well as systolic and diastolic bloodpressure may contain errors as a result of the presence of noise.Detection of ventricular and atrial arrhythmias in ECG may haveexcessive incidence of false positives due to the inability of a signalprocessing algorithm to provide accurate detection, particularly in thepresence of noise. Likewise, the presence of noise may result ininaccurate and inconsistent evaluation of cardiac pathologies that arereflected in ECG morphology. Because of lack of confidence in theaccuracy of results, human review has often been used to confirm resultsor correct errors made by automated analysis algorithms.

Inaccuracies in performance can also result in excess telecom costs whenmonitoring ambulatory subjects. For example, some types of ambulatoryECG monitoring devices employ on-board signal processing to detectarrhythmias and forward the detected arrhythmias to a monitoring centerwhere they are further processed and reviewed by a human being using adata review system. Because of limitations in existing algorithms, thereis a high rate of false positive arrhythmia detections in the ambulatorydevice that results in a high volume of data transmitted from thepatient to a monitoring center. This results in excessivetelecommunications expense, the need for additional memory in theambulatory monitoring device, and additional expense to manually reviewthe data received at the monitoring center.

Various methods of data compression are also limited in their ability toprovide high levels of compression with minimal signal distortion inpart due to the presence of noise. More efficient data compression canreduce the volume of data that must be stored in memory on an ambulatorymonitoring device as well as reduce the volume of data transmitted fromthe monitored subject. In certain applications, this can result in areduction in telecom expense and a reduction in power consumption in theambulatory monitoring device, leading to a reduction in the device sizeand extension of battery life.

The presence of noise in physiological signals can be a limiting factorin providing accurate and consistent computerized evaluations andextraction of information, but the removal of noise has been complicatedby the fact that the noise often has spectral content that falls withinthe bandwidth of the signals of interest (referred to as in-band noise).For example atrial signals can be contaminated by electrical activity ofthe ventricle, and ECG signals can be contaminated by EMG from theskeletal muscles. The plethora of signal sources contained within alimited number of channels measured in a surface ECG, with each channelcontaining mixed interdependent signals, renders problematic theindependent observation of the sources of these multisource signals.This problem is further complicated when signals are acquired fromclosely spaced electrodes and are contaminated by noise, as is usuallythe case when monitoring patients outside a clinic or hospital. Thischaracterization is not only common to electrocardiogram (ECG) signalsacquired with surface or subcutaneous leads but is also common toelectrograms (EGM) measured with intracardiac leads, blood pressuresignals, pulse oximetry signals, peripheral nerve activity (PNA)recordings, signals representing non-invasive measurements ofintracranial pressure, and other physiologic signals collected fromambulatory subjects. Current filtering techniques such as bandpassfiltering are effective in removing noise without distorting the signalwhen the spectral content of the noise and signal are separated in thefrequency domain. Many filtering techniques capable of removing in-bandnoise, such as independent component analysis require that noise andsignal content are uncorrelated and independent, an inaccurateassumption for most physiological signals.

Removing at least some of the in-band noise, or denoising, ofphysiological signals can be useful in the improvement of accuracy ofcomputerized evaluations and has been an objective of many priorefforts. However, the success of many prior efforts has been limited.Various techniques have also been limited in their ability to reportcharacteristics of information derived by an algorithm relative tonoise, artifact, or signal morphology changes. These and other mattershave presented challenges to the design and implementation of devices,systems and methods for processing physiological signals.

SUMMARY

Various aspects of the present invention are directed to devices,methods and systems involving physiological signal processing, in amanner that addresses challenges and limitations including thosediscussed above.

In accordance with various example embodiments, a physiological signalis denoised. In various implementations, denoising is carried out usingtechniques including those referred to herein as Multi-Domain SignalProcessing (MDSP), for which a multitude of exemplary embodiments aredescribed. The captured signal is received in a first domain and isdecomposed into subcomponents in a second domain of higher dimensionthan the first domain. The resulting subcomponents in the second domainare processed based upon the spatial distribution of the subcomponentsusing, for example, spatially selective filtering or principal componentanalysis to identify those subcomponents that are primarily associatedwith noise in a time segment. Subcomponents identified as primarilyassociated with noise are removed and the remaining subcomponents arecombined to reconstruct a denoised signal in the first domain.

Another example embodiment is directed to a method for computing adenoised physiological signal from an input signal including a desiredphysiological signal and noise. The input signal is decomposed from afirst domain into subcomponents of the input signal in a second domainof higher dimension than the first domain. From the decomposedsubcomponents, target subcomponents of the input signal that areassociated with the desired physiological signal are identified basedupon the spatial distribution of the subcomponents. A denoisedphysiological signal is constructed in the first domain from at leastone of the identified target subcomponents.

Another example embodiment is directed to a method for reducing thepower of an undesired signal in a recording of a quasi-periodicmammalian physiological signal containing desired components andundesired components. A transform is used to decompose the physiologicalsignal into subcomponents representing the signal in a time-frequencydomain. At least two time windows are identified in a cycle of thesignal, each time window having a different band of frequenciesassociated with desired components of the signal. In the at least twotime windows, ones of the subcomponents associated with the desiredsignal components are identified as subcomponents having spectralcontent overlapping a band of frequencies associated with desired signalcomponents within said at least two time windows. A denoisedphysiological signal is constructed using at least one of the identifiedsubcomponents within each of the at least two time windows.

In another aspect of the present invention, a dynamic signal-to-noiseratio is computed as the ratio of power contained in the subcomponentsprimarily associated with signal and subcomponents primarily associatedwith noise.

In another aspect of the present invention, the subcomponents associatedwith a signal wave of a physiological signal are denoised and arecombined to form a denoised emphasis signal of the signal wave. Theemphasis signal is subsequently evaluated to identify feature points ofthe signal wave.

In another aspect of the present invention, feature points of signalwaves are evaluated to detect clinically significant events and signalmorphology characteristics.

In another aspect of the present invention, a validity metric, computedas a function of a dynamic signal-to-noise ratio and signal morphologycharacteristics, is used to assess the accuracy, consistency, andvalidity of feature points, morphology characteristics, and detectedevents.

In yet another aspect of the present invention, the denoised signal orits subcomponents and detected features are used to facilitate signalcompression to reduce stored and communicated data volume.

In yet another aspect of the present invention, signal compression andfeature and event detection improvements are used to implement a deviceof reduced size and enhanced performance.

The above summary is not intended to describe each embodiment or everyimplementation of the present disclosure. The figures and detaileddescription that follow more particularly exemplify various embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be more completely understood in consideration of thefollowing detailed description of various embodiments of the inventionin connection with the accompanying drawings, in which:

FIG. 1 shows a data flow diagram of Multi-Domain Signal Processingapplied to denoising a captured signal, consistent with an exampleembodiment of the present invention;

FIG. 2 shows a data flow diagram of Multi-Domain Signal Processingapplied to extraction of a signal from a captured physiological signal,consistent with an example embodiment of the present invention;

FIG. 3 shows scatter plots, in which FIG. 3a shows a scatter plot ofsubcomponents of a noisy signal before application of PCA and ICA, andFIG. 3b shows a scatter plot of subcomponents of the same noisy signalof FIG. 3a after application of PCA and ICA, according to anotherexample embodiment;

FIG. 4 illustrates a data flow diagram for computing a dynamicsignal-to-noise ratio, according to an example embodiment of the presentinvention;

FIG. 5 shows an example of denoising performance and QRS detection in arabbit ECG with non-sustained ventricular tachycardia and illustrates adynamic signal-to-noise ratio updated for every cardiac cycle, accordingto an example embodiment of the present invention;

FIG. 6 shows an example of denoising performance on 3-lead ECG signalsusing PCA, ICA, and MDSP, according to another example embodiment of thepresent invention;

FIG. 7 shows results representing denoising performance of MDSP, PCA,and bandpass filtering, in connection with another example embodiment ofthe present invention;

FIG. 8 shows an example of denoising performance on a single channelnoisy ECG signal, in connection with an example embodiment of thepresent invention;

FIG. 9 shows an example of atrial activity extraction from a surface ECGsignal containing atrial flutter, in connection with another exampleembodiment of the present invention;

FIG. 10 shows an example of atrial activity extraction from a surfaceECG signal containing atrial fibrillation, in connection with anotherexample embodiment of the present invention;

FIG. 11 shows an example of denoising performance on single channelperipheral nerve activity (PNA) recording corrupted with noise, inconnection with another example embodiment of the present invention;

FIG. 12 shows a data flow diagram for computing an emphasis signal andfinding a feature point in a signal waveform, and example signalwaveforms and corresponding emphasis signals and feature point markers,according to another example embodiment of the present invention;

FIG. 13 shows an example of QRS detection performance for a challengingECG recording, according to another example embodiment of the presentinvention;

FIG. 14 shows a data flow diagram for computing an emphasis signalcorresponding to a respiration pattern, according to an exampleembodiment of the present invention;

FIG. 15 shows an example of respiration signal extraction in an ECGrecording, according to another example embodiment of the presentinvention;

FIG. 16 illustrates an apparatus for efficient wireless communication ofan ECG along with data flow diagrams for ECG signal compression,according to an example embodiment of the present invention;

FIG. 17 shows an example of 3D ECG cycle plot as an intermediate step inECG compression, in connection with another example embodiment of thepresent invention;

FIG. 18 shows a system for evaluating ECG strips captured by anambulatory monitoring device along with data flow diagrams fordetermining if a strip contains an arrhythmia and assessing the validityof the result, according to an example embodiment of the presentinvention;

FIG. 19 illustrates an apparatus for improving the signal-to-noise ratioof an ECG signal and related flow chart for processing an ECG signal,according to an example embodiment of the present invention;

FIG. 20 illustrates an approach for spatially selective filtering, andpartitioning of a cardiac cycle of an ECG signal, according to anotherexample embodiment of the present invention;

FIG. 21 illustrates a signal flow diagram for an embodiment of denoisingapplied to an ECG signal, according to another example embodiment of thepresent invention; and

FIG. 22 illustrates a system for denoising an ECG signal, according toanother example embodiment of the present invention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe scope of the invention including aspects defined in the claims.

DETAILED DESCRIPTION

Various example embodiments of the present invention relate to theprocessing of physiological signals, and in many implementations, toreduce noise, extract information, characterize signals, and/or compressthe volume of data. While the present invention is not necessarilylimited to such applications, various aspects of the invention may beappreciated through a discussion of examples using this context.

Many embodiments described here are broadly referred to as including anapproach referred to as “Multi-Domain Signal Processing” (MDSP), forwhich many different embodiments are described by way of example. Inconnection with various embodiments, the term Multi-Domain Filtering(MDF) is used herein to refer to embodiments that use one or moreMDSP-based embodiments to denoise physiologic signals. Variousembodiments are also directed to processing a broad range ofphysiological signals including but not limited to signals correspondingto ECG, blood pressure, respiratory, heart sounds, EEG, peripheral nerveactivity, atrial activity, temperature, photoplethysmography, tissueimpedance, blood glucose, and EMG. Different embodiments are furtherdirected to one or more of: improving the accuracy and consistency ofinformation provided under a broad range of use scenarios, extendingbattery life of monitoring devices, providing a desirably-sizedmonitoring device (e.g., a reduced, or small size), and improving and/orreducing the need for human review of analysis results and theassociated expense of doing so.

In the following discussion, reference is made to cited referenceslisted in a numbered order near the end of this document, which arefully incorporated herein by reference. These references may assist inproviding general information regarding a variety of fields that mayrelate to one or more embodiments of the present invention, and furthermay provide specific information regarding the application of one ormore such embodiments.

According to an example embodiment, physiological signals of a subjectare captured by an implantable or external device. This device can beimplemented as a part of a system that measures, processes, andevaluates physiological data from animal or human subjects for research,therapy titration, diagnosis, or delivery of medical care. The devicemay temporarily store the processed signals for later transmission to asystem remote from the subject for further processing, display, andreporting to a medical care provider or researcher. The processedsignals may be transmitted in real time or they may be used within atherapy delivery device as part of a system to control or adviseadministration of a therapy. In yet another embodiment, unprocessed orpartially processed signals may be transmitted to a device or systemlocated outside the body of the subject for processing, display, review,reporting, or retransmission to another system.

In many applications, physiological signals processed in accordance withthe embodiments discussed herein are multisource signals, meaning thatthe signal observed by electrodes or leads includes components that arethe result of many physiological processes and sources that are ofteninterdependent. For example, an ECG signal measured at the surface ofthe body may include signals emanating from sources such as the atria,ventricles, electrical noise (e.g., from sources outside the body),noise from muscular electrical activity, signals resulting frompathologies such as conduction defects, scars in tissue from myocardialinfarction, ischemia resulting from reduced blood flow to a region ofthe heart, and other representative examples. In these and othercontexts, various embodiments of the present invention are directed toaddressing challenges relative to the analysis of ECG signals, such asthose that benefit from independent analysis for denoising andevaluation of cardiac function.

The terms “quasi-periodic”, “signal wave”, “feature point”, “parameter”,and “event” are used in connection with the discussion of variousembodiments as follows. The term quasi-periodic refers to a periodicsignal with a period and with a cycle length that varies with time, anda signal wave is a particular portion or aspect of a period of aphysiological signal. For example, an ECG signal may include signalwaves referred to as P, Q, R, S, T and U waves. Another example signalwave is the QRS complex portion of an ECG (e.g., the portion of an ECGsignal corresponding to the depolarization of the left and rightventricles). In an arterial pressure signal, the dicrotic notch is asignal wave.

Regarding the term “feature point,” many physiological signals can becharacterized as having features and parameters. A feature point is anidentified point within a physiological signal, which may be useful forcharacterizing the signal and related characteristics of the signal'ssource. For instance, in heart-related signals, such as ECG, arterialpressure, and blood flow, most cardiac cycles have a feature point orpoints of interest. Examples include a point corresponding to the onsetof the Q-wave (e.g., Q-wave onset) or offset of the T-wave (e.g., T-waveoffset) in an ECG or systole in an arterial blood pressure signal. Eachof these feature points is described by time of occurrence andamplitude, and consecutive feature points can be combined to form afeature signal.

With respect to the term “feature point,” it is sometimes useful tocombine feature points over a predetermined period of time, referred toas a feature signal, to compute a parameter. For example, systolefeature points can be combined to compute a mean systolic pressure.Computing a parameter can have the effect of reducing or eliminatingshort-term physiological fluctuations (e.g., changes with respiration)in the feature signal that are not of interest to the user.

Regarding the term “event,” physiological signals may includeinformation that can be used for identifying the onset and offset of anevent, or simply the fact that an event occurred. For example, whenmonitoring the ECG of a subject it may be useful to know that anarrhythmic event such as ventricular tachycardia or atrial fibrillationhas occurred. In additional embodiments, information is combined frommultiple signals to compute features and parameters. For example, the QAinterval (the time difference between Q-wave onset and the upstroke ofan arterial pressure wave) can be used as a surrogate for cardiaccontractility, and employs both a pressure and an ECG signal from asubject. QA interval feature points are computed for a cardiac cycle,can be used to create a feature signal, and averaged over apredetermined period of time to create a QA parameter.

Regarding the terms referring to a “desired physiological signal,” sucha signal refers to a signal that is to be extracted. This signal maycorrespond, for example, to a physiological signal within an inputsignal that includes the physiological signal and noise. In thiscontext, the noise may also include a physiological signal that is notthe desired physiological signal. For instance, where a desiredphysiological signal is an ECG signal from a subject's heart, otherphysiological signals included with an input signal, such as arespiratory signal, are not desirably extracted.

A “captured signal” is a signal that is sensed and recorded (e.g.,digitized), and may also be conditioned. Conditioning of the signal mayinvolve amplification and the application of a filter to remove much ofthe noise that is outside the bandwidth of the signal. Followingdigitization, additional filtering may be applied to further removenoise from outside the signal bandwidth, typically using linearfiltering techniques such as a finite impulse response filter. A“denoised” signal is a captured signal that has been processed to removenoise, such as by removing undesirable signal components orsubcomponents having spectral content within a bandwidth of a selected(e.g., target or desired) signal, or in-band noise. Denoising can beuseful for rendering clarity to the desired signal as a result ofsuppression of undesired signal components (e.g., noise), hence makingthe desired signal more suitable for analysis.

In accordance with other example embodiments of the present invention,in-band noise of a physiological signal is reduced using a techniqueinvolving a multi-domain filtering-type of approach, referred to inconnection with various embodiments as MDF. Resulting signals are thusdenoised, in the context that at least some noise components in thephysiological signal have been removed, relative to the resulting signal(e.g., as re-generated from components of the physiological signal, in adifferent domain). In one embodiment, signals captured in a first domainare decomposed into subcomponents in a second domain of higher dimensionthan the first domain. To remove in-band noise from the capturedphysiological signal, the subcomponents in the second domain areprocessed based on their spatial distribution using, for example,spatially selective filtering or principal component analysis toidentify those subcomponents that are primarily associated with noise atan instant in time. Those subcomponents that are identified as primarilyassociated with noise are removed and the residual subcomponents (thosenot having been removed) are combined to reconstruct a denoised signalin the first domain. A subcomponent occurring in a time window within acycle of the pseudo-periodic signal is said to be associated with asignal if it contains frequencies within a band that has previously beencharacterized as being present in the signal. A subcomponent within atime segment is considered to be primarily associated with noise if theenergy associated with the signal is attributed primarily (e.g., morethan half) to noise. In some embodiments, a larger tolerance for noiseis set, to permit signals with larger noise content to be processedwithout distorting the signal (e.g., where a desired signal is expectedto include a substantial portion of noise). Under such conditions,processing of the detected signal can be carried out with theunderstanding that noise forms a majority of the power in thesubcomponent. In some implementations, a signal is treated as primarilyassociated with noise when at least about 60% of the signal's energy atthat time segment is noise energy. Likewise, a subcomponent within atime segment is considered to be primarily associated with signal if atleast half or more (e.g., 60%) of its energy is within the band offrequencies characterized as being present in the signal.

In the context of various embodiments, references to the removal ofsubcomponents may not involve any removal or modification of thesubcomponents, but rather involve a selective combination of thosesubcomponents that have been determined to be desirable. For example,for a physiological signal having various subcomponents, certaincomponents can be identified as likely representing components of asignal corresponding to a particular physiological characteristic thatis to be analyzed. These identified (desirable) components can beselectively combined, leaving behind other subcomponents. In thiscontext, undesirable subcomponents are not necessarily removed, butrather, have not been used when forming a recombined signalcorresponding to a received physiological signal.

The subcomponents that result from decomposition in Multi-Domain SignalProcessing (MDSP) embodiments are also used in other aspects ofphysiologic signal processing, for various embodiments. A characteristicof MDSP is that a signal wave in a multisource physiological signal canbe represented using a small number of subcomponents that contain mostof the energy of the signal wave. For example, in an ECG decomposedusing a discrete wavelet transform, a group of 3 subcomponents maycontain most of the energy found in the P-wave of an ECG while most ofthe energy of a QRS complex may be contained in 4 subcomponents. Asubcomponent or its time segment containing a significant amount ofenergy of a signal at that time segment is said to be associated withthe signal at that time segment. As applicable here, a significantamount of energy of a subcomponent is an amount of energy, correspondingto the time segment, that is at least half of the total energy of thesignal during the time segment. In various implementations, asubcomponent may be associated with more than one signal wave.

Subcomponents or their time segments associated with a signal wave canbe identified, isolated, and used to construct a signal wave independentof other signal waves in a multisource signal. For example,subcomponents associated with a P-wave of an ECG can be identifiedwithin the second domain independent of ventricular electrical activity,and used to reconstruct a denoised signal representative of atrialactivity. Another group of subcomponents is associated with the T-waveof an ECG, and are used in the extraction of repolarization activity.Signal source extraction of the signal subcomponents in a multisourcesignal can lead to more accurate analysis and evaluation of certainphysiological functions. Another embodiment involving signal sourceextraction is directed to the extraction of fetal ECGs from ECG signalscaptured from a pregnant female. Yet another example embodiment isdirected to extracting an oxygen saturation signal from aphotoplethysmography signal.

Subcomponents can also be used to compute a denoised emphasis signalthat exaggerates the features of particular signal waves to facilitatethe identification and detection of feature points. For example,subcomponents associated with the T-wave of an ECG can be identified andused to compute a denoised emphasis signal that exaggerates the T-waverelative to other ECG signal waves to facilitate accurate and consistentdetection of T-wave offset.

In some embodiments, physiological signal subcomponents are used tocompute a dynamic signal-to-noise ratio (dSNR) that represents the ratioof signal energy relative to noise energy on a sample-by-sample basis,or longer period of time such as for one or more cardiac cycles of anECG. The dSNR can be used for computing a validity signal for assessingthe accuracy and reliability of information derived from the capturedphysiological signal. In some embodiments, the dSNR is used to directlyassess the accuracy and reliability of derived information. If dSNR fora cardiac cycle or a portion of a cardiac cycle is low, for example,certain information derived from the ECG for that cardiac cycle may notbe accurate and, if it is very low, a cardiac cycle may beuninterpretable.

MDF denoising and other aspects of one or more MDSP-based embodimentsare directed to facilitating the efficient compression of physiologicalsignals, which can be used to reduce the volume of data corresponding tosensed signals. Reducing the amount of data (e.g., by eliminating oromitting noise) serves to help the efficient storage of data, and alsoto reduce the amount of data needed in communications which can behelpful, for example, to simplify wireless transmission protocol. Forexample, various MDSP-based embodiments involve compressing ECG signalsat compression rates of 15:1 to 20:1 (e.g., relative to an originalphysiological signal prior to denoising) with minimal signal distortion.An MDSP approach is used to achieve accurate identification of cardiaccycles of a denoised ECG, and significantly reduce in-band noise. Theseidentification and denoising approaches are used in connection with oneor more of a variety of compression algorithms, in accordance withvarious embodiments, together with identified redundancies in adjacentcardiac cycles to achieve efficient compression.

In some embodiments, an MDF-based approach is used to remove noise fromperipheral nerve activity (PNA) signals and facilitate the accuratequantification of PNA. In these and/or other embodiments, an MDSP-basedapproach is used across a broad range of physiological signal processingapplications with related processing without necessarily relying uponassumptions regarding the independence of signal and noise sources,and/or based upon an assumption (for processing) that physiologicalsignals being processed are quasi-periodic signals.

In the following discussion, reference is made to various documents orother references listed near the end of this patent document, bynumerals within square brackets. The information in thedocuments/references to which these numerals refer may be implemented inconnection with one or more example embodiments, and are fullyincorporated herein by reference.

Various embodiments are directed to processing signals from ambulatorysubjects under conditions in which noise is common and measurements areoften obtained using a limited number of closely-spaced electrodes, or asingle cannulation of a vessel, such as in the case of blood pressuremeasurements. Such signals often include aspects of signals emanatingfrom multiple sources that are generally interdependent. For example,heart activity in an atrial chamber usually initiates activity in aventricular chamber. Statistically, this situation is characterized bymutual dependency of signal sources contaminated by noise. This violatesa central assumption of independence of sources that is fundamental tosuccessful application of many current techniques used to separatesignal source and noise in multisource signals, such as independentcomponent analysis (ICA) [1]. Various embodiments are directed toprocessing related signals, without necessarily assuming suchindependence, to obtain a denoised signal for a desired signal (i.e., asignal corresponding to a characteristic to be detected). The problem ofextracting source signals from the multisource physiological signalscontaminated with noise can be expressed mathematically as:x(t)=As(t)+n(t)  (1)where s(t)=(s₁ (t), . . . , s_(P)(t)) is a vector of source signals thatare mixed together by an unknown mixing matrix A of size M×P, where M isa number of observed signals and P is a number of source signals andadditive noise n(t)=(n₁(t), . . . , n_(P)(t)). The extraction of sourcesignals is achieved by estimating the inverse of a mixing matrix A andcomputing the denoised and separated signals s(t)=(s₁(t), . . . ,s_(P)(t)) from the observed signals x(t)=(x₁(t), . . . , x_(M)(t))according to the formula s(t)=A⁻¹x(t)−A⁻¹n(t). When the number of leadsM is less than the number of sources P, which is most often the case inphysiologic monitoring, the mixing matrix A_(M×P) is underdetermined. Inaddition, the sources that make up multisource physiological signals areoften not independent. In this situation the matrix A_(M×P) is notinvertible, rendering it difficult to estimate an inverse of the M×Pmixing matrix and recover unmixed sources from mixed observations, as isthe case when using ICA techniques.

Turning now to the figures, FIGS. 1 and 2 show signal denoising andsignal source extraction, as carried out in a three-step process inaccordance with another example embodiment of the present invention. Thefirst step, 102 and 202 in FIGS. 1 and 2 respectively, involvesdecomposing an input signal 101 and 201 into subcomponents in a seconddomain of larger dimension than the first domain. Decomposition steps102 and 202 are performed using one of a variety of transforms that isused for accurate signal reconstruction. Such transforms may involveusing, for example, a discrete cosine transform, a wavelet relatedtransform, a Karhunen-Loeve transform, a Fourier transform, a Gabortransform, or a filter bank. In FIGS. 1 and 2, the set of subcomponentsresulting from the decomposition steps 102 and 202, respectively isreferred to as D2sub. The dimension of the first domain is defined bythe number of observed, or captured, signal channels. The dimension ofthe second domain is defined by the number of channels multiplied by thenumber of subcomponents in each channel. The second step involves theidentification of signal and noise subcomponents in the second domainfollowed by removal of unwanted subcomponents, including noise.

Referring to FIG. 1, to denoise a desired signal, subcomponents of theset D2sub that are primarily associated with noise are identified andremoved to create a subset D2s of residual denoised subcomponents.Referring to FIG. 2, if signal source extraction is performed, such aswhen an atrial activity signal is extracted from a surface ECG, thesubcomponents associated with the signal sources to be extracted areidentified and the other subcomponents are removed to create a subsetD2soi containing energy associated with the desired signal. In certainembodiments involving both denoising and signal source extraction,subcomponents to be removed are identified based upon their spatialdistribution using, for example, Spatially Selective Filtering (SSF) orPeriodic Component Analysis (TiCA). At decision steps 103 and 203 inFIGS. 1 and 2, the signal is processed at either 104, 204 if thedimension of the first domain is greater than 1, or at block 105/205 ifthe dimension of the first domain is not greater than one.

If the dimension of the first domain is greater than 1, then SSF, πCA,Principal Component Analysis (PCA), and Independent Component Analysis(ICA) can be used in combination (e.g., as in blocks 104 and 204). Ifthe dimension of the first domain is equal to 1, then SSF or PCA can beused to evaluate the spatial distribution of the subcomponents, as in105 and 205. Once the subcomponents associated with noise are identifiedthey are effectively removed by setting them to zero. Following removalof the subcomponents associated with noise, the subcomponents are saidto be denoised. In the case of signal source extraction, thosesubcomponents associated with noise and those not associated with asignal source to be extracted are identified and effectively removed bysetting them to zero.

In steps 106 and 206, the denoised signal subcomponents, referred to assubset D2s, are reconstructed in the first domain to provide denoisedoutput signal 107 by performing the inverse of the transform used todecompose the signal. If signal source extraction is performed onlythose subcomponents not removed and identified as associated with thedesired signal sources, referred to as subcomponent subset D2soi, arereconstructed into the extracted desired signal 207 in FIG. 2.

Various embodiments directed to MDSP approaches use a mathematicalrepresentation of physiological signals as a linear combination of basisfunctions. Each of these basis functions is chosen to fit a prominentfeature, or signal wave, of a signal source. For example, a basisfunction might represent a type of QRS complex contained in an ECGsignal. In one embodiment, decomposition is achieved by representing theobserved signals as a linear combination of basis functions,

$\begin{matrix}{{{s_{i}(t)} = {\sum\limits_{k = 1}^{K}{{\varphi_{k}(t)}d_{ik}}}},{i = {1\text{:}P}},{{x_{i}(t)} = {\sum\limits_{k = 1}^{K}{{\varphi_{k}(t)}c_{ik}}}},{i = {1\text{:}M}}} & (2)\end{matrix}$

where d_(ik) and c_(ik) are decomposition coefficients or subcomponents.In some embodiments, the elements φ_(k) may be mutually dependent andform an overcomplete set referred to as a “dictionary” D. The elementsare selected to provide a sparse representation of the signal sources,meaning that most of the decomposition coefficients d_(ik) and c_(ik) inequation (2) are close to or equal to zero. A combination of basisfunctions or elements of the dictionary forms the second domain.

Higher sparsity provides for greater statistical independence of signalsource and noise subcomponents. This property of sparse decomposition isused to facilitate effective identification and extraction of signalsource subcomponents and removal of noise. Sparse decomposition, forexample, can result in a concentration of signal energy in a relativelysmall number of large decomposition coefficients while noise is spreadout across many basis functions and represented by small decompositioncoefficients as described in equation (2).

The sparsity criteria can be used to choose a dictionary that providesfor sparse decomposition of the signal sources and hence improved signalsource extraction and denoising. In certain embodiments it may beadvantageous to choose an over-complete dictionary D in order toincrease the degree of sparsity of the decomposition. The examples ofdictionaries that are relevant to this embodiment include eigenfunctions[2], wavelet related transforms, including wavelet packets andshift-invariant or stationary wavelets [3], cosine transform [4],Fourier basis [5] or Gabor basis and their combinations [6]. A customdictionary that achieves a higher degree of sparsity can also bedesigned by a number of methods for finding basis functions thatrepresent building blocks of the signal [7]. The examples of thesemethods are matching pursuit [5], orthogonal matching pursuit [8], basispursuit [9], K-SVD [10], bounded error subset selection [11] ororthogonal subspace pursuit. In general, the dictionary design methodsuse a representative set of training signals, to find a dictionary thatleads to the best representation for each signal wave in this set, understrict sparsity constraints:

${\min\limits_{D,c}{\left\{ {{x - {Dc}}}_{F}^{2} \right\}\mspace{14mu}{subject}\mspace{14mu}{to}\mspace{14mu}{c}_{0}}} \leq T$where ∥.∥₀ is a l⁰ norm, counting the nonzero entries of a vector, T isa constraint on the maximum number of non-zero elements allowed, and∥.∥_(F) ² is a Frobenius norm. For example, a representative set oftraining signals can be a set of ECG recordings with a broad range ofmorphologies that include normal sinus rhythm, conduction abnormalities,pathologies, arrhythmias for a particular species. The set shouldinclude a sample of each morphology that the algorithm may experienceduring analysis of an ECG recording of the species. In one embodiment,the custom dictionary is built using the following iterative procedure.A vector is selected from the training set and is represented as asparse linear combination of initial dictionary elements. This can beaccomplished, for example, by finding elements that are maximallycorrelated with the vector. This subset of elements form a subspace thatis decorrelated from the rest of the training set (e.g., by usingsingular value decomposition (SVD)). The decorrelated subset is added tothe dictionary and the elements of the training set that lie in thissubspace are removed from the training set. The process is repeateduntil an error threshold is reached or an allowed number of iterationsis exhausted.

In another embodiment decomposition into the second domain is achievedby applying an adaptive filter that automatically selects the frequencybands, or subbands of wavelet filters or a filter bank, in whichsubcomponents of the signal sources are most independent. Then eachsubcomponent x_(i) (t),i=1:M, can be represented as a sum of subbandsignalsx _(i)(t)=x _(i,1)(t)+ . . . +x _(i,K)(t)  (3)where K is the number of chosen subbands. The filter coefficients can beadaptively tuned to reduce and/or minimize the mutual information andincrease independence between the filter outputs as described by Zhang,et al. [12]

In an embodiment involving a wavelet-related transform decomposition,the first decomposition step for an observed signal with M channels intoa wavelet-related dictionary D can be formally described as iterativemultiplication of the observed signal by an operator matrix W^(j) witheach iteration step j making up a decomposition level. In thisembodiment, the operator matrix W^(j) is a Toeplitz matrix with theentries formed by a wavelet impulse response. The size of the matrixoperator W^(j) is N×N where N is the number of samples of the observedsignal. N is greater than both the number of sources P and the number ofobserved signals M. Wavelets may be used to generate subcomponentshaving characteristics of both sparse and subband decomposition. In someimplementations, a wavelet decomposition is computed via iterativeconvolution with wavelet filters or filter banks as described byVaidyanathan[13].

Referring again to FIGS. 1 and 2, another example embodiment isimplemented at step 2, for an MDSP-based approach. The statisticalindependence of at least some subcomponents related to signal sourcesand noise is leveraged to identify those subcomponents associated withsignal sources and those associated with noise. For example, in case ofwavelet decomposition, the equation (1) becomesW _(x) ^(j) =A _(j) W _(s) ^(j) +W _(n) ^(j) , j=1:NL+1  (4)where NL is the number of decomposition levels or subbands. The mixingmatrix A is indexed by j, denoting that the sources are mixeddifferently in each subband according to their spectral distribution.

The subcomponents associated with signal sources can be identified basedon spatial distribution of subcomponents in time using one of severaltechniques, many of which involve finding the mixing matrices A_(j) andthen inverting them to calculate the signal source subcomponents.Examples of techniques that are applicable to identification ofsubcomponents associated with noise and subcomponents associated withsignal sources include principal component analysis (PCA), independentcomponent analysis (ICA), periodic component analysis (πCA) techniquesand spatially selective filtering as shown in blocks 104 and 105 of FIG.1 and blocks 204 and 205 of FIG. 2. These techniques can be usedseparately or in combination to achieve acceptable performance.

When using the PCA technique to identify subcomponents associated withsignal sources, an inverse of the mixing matrix A_(j) is estimated [14,15] using singular value decomposition or eigenvalue decomposition ofthe covariance matrix of signal subcomponents.

This PCA approach involves rotating and scaling the data in order toorthogonalize independent subcomponents. The orthogonalizedsubcomponents with low signal power are often associated with noise andcan be removed to achieve denoising. PCA can be used as a preliminarystep prior to applying ICA technique.

In various embodiments involving the use of an ICA technique to identifysubcomponents associated with signal sources, an inverse of the mixingmatrix A_(j) is estimated [4,16] as a solution of an optimizationproblem that maximizes independence between the signal sources. Forexample, ICA techniques can use either higher-order statistics of signalcomponents [17, 18] or information-theoretic criteria to maximizeindependence. Information theoretic criteria that can be applied includemaximization of negentropy or its approximation [19], minimization ofmutual information [19], maximum likelihood estimation [20, 21], maximuma posteriori probability [22], or expectation-maximization of Gaussianmixture models of sources [23]. These solutions can be approximated viaefficient numerical methods, such as FastICA [24] and JADE [19]algorithms. The FIG. 3a provides a scatter plot image of subcomponentsin the second domain prior to the application of PCA and ICA in thedenoising process. FIG. 3b demonstrates an example result of denoisingand subcomponent separation achieved by applying a combination of PCAand ICA techniques. Note that application of PCA and ICA has achieved adecorrelation of the subcomponents and alignment along the coordinateaxis 302 of the scatter plot, indicating a large degree of independencehas been achieved between the subcomponents (e.g., relative to alignmentwithin coordinate axis 301 FIG. 3a ). This high degree of independencebetween subcomponents that occurs as a result of the denoising processfacilitates efficient removal of noise and signal source extraction.

In embodiments involving the use of πCA technique to identify signalsources, an inverse of the mixing matrix A_(j) is estimated as asolution of an optimization problem that separates subcomponents basedon their periodicity or quasi-periodicity [25, 26]. Instead ofdiagonalizing and inverting the covariance matrix as is done with PCA,the πCA technique jointly diagonalizes the covariance matrix and anautocorrelation matrix. The autocorrelation matrix is calculated as anaverage of autocorrelation matrices computed over time lagscorresponding to period lengths.

In one embodiment, a quasi-periodic signal can be phase-wrapped bymapping the period length to a linear phase φ(t) ranging from −π to +πassigned to each sample. The autocorrelation matrix can then becalculated in the polar coordinates in which cardiac cycles are phasealigned. The technique involves extracting most pseudo-periodicsubcomponents corresponding to a desired physiologic signal. Thetechnique is efficient at identifying signal source and noisesubcomponents but relies on accurate detection of cycles such as cardiacor respiratory cycles. It can be used in combination with spatiallyselective filtering (SSF), a technique that facilitates better cycledetection.

In another embodiment, referring to FIG. 2, signal sources areidentified and separated in the second step, blocks 204 and 205 asdiscussed above using spatially selective filtering. Spatially selectivefiltering [27, 28, 29, 30] techniques detect signal-related features andpass them across the subcomponents while blocking features inherent tonoise. The technique relies on the differences of noise and signaldistributions between subcomponents. In one embodiment, spatiallyselective filtering is facilitated by a decomposition whereby signalenergy is concentrated in a small number of large subcomponentcoefficients while noise is spread out across many decomposition levelsand is represented by small coefficients. Techniques similar to waveletthresholding [31] can be used to remove this noise. This may result in aslight degradation of signal morphology.

In another embodiment, spatially selective filtering is used to exploitthe fact that most noise subcomponents are confined to decompositionlevels that represent high frequencies. In this embodiment the locationsof signal features are identified by examining subcomponentscorresponding to lower frequency or correlation between subcomponents.For example, QRS wave location can be identified as high amplitude peaksand valleys that occur simultaneously across multiple subcomponentsassociated with lower frequencies. The rest of the ECG waves (P, T, U)have lower frequency content.

Referring to FIG. 20, a cardiac cycle of an ECG is partitioned into timewindows 2002 and 2003: 2002 containing the QRS complex and 2003 spanningthe remainder of the cardiac cycle. The subcomponents associated withboth low and high frequencies, represented by region 2004, are preservedwithin the time window 2002 containing the identified QRS complex. Thelow-frequency subcomponents associated with the desired signal, presentin window 2003 and represented by region 2005, are preserved andsubcomponents associated with high frequencies are removed. In anotherembodiment, subcomponents associated with low-frequencies, representedby region 2007, are preserved throughout the cardiac cycle andsubcomponents associated with high frequencies are preserved in a timewindow represented by region 2006. In another embodiment, in signalswith low amplitude subcomponents that are associated with high frequencysuch as PNA, high amplitude EMG noise can be detected as large peakspresent in subcomponents corresponding to high frequency. The EMG noisecan then be removed by zeroing subcomponents in the time window wherethe large peaks were detected. In another embodiment, the high amplitudenoise or artifact is detected by identifying undesired peaks insubcomponents that correspond to frequencies that do not overlap theband of frequencies associated with the desired signal. For example, ablood pressure signal can be denoised using this approach.

In some embodiments the spatially selective filtering is combined withone or more of PCA, ICA, or πCA. For example, the subcomponents thatwere identified as noise at the PCA stage can be further filtered toremove only some segments that have spatial and temporal characteristicsof noise. In another example, the subcomponents that were identified assignals at the PCA stage can be further filtered to remove additionaltime segments from subcomponents that have spatial and temporalcharacteristics of noise.

The choice of a technique for identification of subcomponents fordenoising or signal source extraction depends upon signal and noisecharacteristics of the physiologic signal, spatial distribution of itssubcomponents and the signal acquisition and processing environment. Forexample, it may be useful to process multi-channel signals with PCA andICA techniques and their combinations with spatially selectivefiltering. When processing ECG signals obtained from an implanted singlelead device spatially selective filtering or πCA may be applied, as inblocks 105 and 205 in FIGS. 1 and 2, respectively.

In some embodiments, step three as shown in blocks 106 and 206 of FIGS.1 and 2, respectively, involves reconstructing the subcomponentsremaining following denoising and signal source extraction, s(t)=(s₁(t),. . . , s_(P)(t)) in the first domain. This is accomplished by aninverse transform of extracted signal sources or denoised signalsources. If wavelet-based decomposition is employed, the reconstructionstep can be formally described as iterative multiplication by an inverseof the operator matrix W^(j) with each iteration step j corresponding toa reconstruction level.

Referring to FIG. 4, other example embodiments involve computing adynamic signal-to-noise ratio (dSNR) in a manner that can be updated ona sample-by-sample basis. dSNR can be used to assess the accuracy andreliability of information derived from the physiological signal and, insome embodiments, can be combined with other signal information such asECG arrhythmic event information to compute a validity metric to assessthe validity of information extracted from a signal. If dSNR for acardiac cycle is low, for example, information derived from the ECG forthat cardiac cycle may not be accurate.

A dynamic signal-to-noise ratio (dSNR) can be computed as the ratio ofthe energies in signal and noise subcomponents. In one embodiment,referring to FIG. 4, an input signal 401 in a first domain is decomposedin process 402 into subcomponents, referred to as set D2sub, in a seconddomain of higher dimension. At decision point 403, the signal processingflow proceeds according to the dimension of the first domain. If thedimension of the first domain equals 1, then subcomponents primarilyassociated with noise, referred to as set D2n, are identified in process405 using SSF or πCA. Subcomponents of set D2n are combined to computean estimate of noise energy and residual subcomponents, referred to asset D2s, are combined to compute an estimate of signal energy in process407. The SNR is then computed in process 409 to create output dSNR value410 according to formula:

${SNR}_{d\; B} = {{10{\log_{10}\left( \frac{P_{signal}}{P_{noise}} \right)}} = {20{\log_{10}\left( \frac{A_{signal}}{A_{noise}} \right)}}}$Where P_(signal) and P_(noise) are respective signal and noise energyand A_(signal) and A_(noise) are respective signal and noise amplitude.

If the dimension of the first domain is larger than one, a PCA or ICAtechnique can be used in process 404 for initial subcomponent denoising.The noise subcomponents extracted at this initial denoising step can bediscarded and the residual noise and signal subcomponents can beidentified using SSF or πCA in process 406. In process 408, the noisesubcomponents identified at this stage, D2n, are used to calculate anestimate of noise energy and the residual subcomponents, D2s, are usedto compute an estimate of signal energy. The SNR is then computed inprocess 409 (e.g., as described above).

In other embodiments, a signal-to-noise ratio is estimated usingconventional approaches following denoising of the signal using an MDSPembodiment. In one embodiment, the noise is measured between signalwaves by computing the peak amplitude and density of zero crossings orvariability of signal in a segment between signal waves. In otherembodiments, a signal-to-noise ratio is estimated by computing aspectral distribution of the denoised signal. For example, in an ECGsignal, peaks in the spectral distribution are evaluated to determinethe relative power in the spectrum that occurs within and outside of thenormal range of the QRS complex, T-wave, and P-wave.

In one embodiment dSNR is updated for each cardiac cycle. Alternativeimplementations may update dSNR more or less often. For example, it maybe useful in some embodiments to compute a value of dSNR for a window oftwo to ten cardiac cycles and use this value in calculation of thevalidity metric for all cardiac cycles within the window.

FIG. 5 provides an example of a dynamic confidence signal (dCS) derivedfrom dSNR computed for a rabbit ECG signal 501, with dCS shown in thedashed line 505 in the top plot, and the bottom plot showing automaticmarking of QRS complexes (squares as 504) and detected VT (circles as503), in accordance with another example embodiment. The dynamicconfidence signal 505 (dashed) is updated for each cardiac cycle. ThedCS is updated every cardiac cycle and the value of dCS reflects changesin signal amplitude relative to the level of noise in the recording.Despite the low signal-to-noise ratio, all QRS complexes (as indicatedby the squares 504 marking bottom ECG trace) and non-sustained episodesof ventricular tachycardia (marked by circles 503 marking bottom ECGtrace) are detected.

In various embodiments, MDSP-based approaches as discussed herein areused to denoise and extract information from a physiologic signalacquired in a low SNR environment. For example, a collar orspring-loaded clip placed on the neck of an animal with embedded ECGelectrodes can be used to collect ECG signals in animals non-invasively.While such an approach may result in a low SNR, denoising approachessuch as discussed herein can be used to glean meaningful data from theECG signals. Another embodiment is directed to placing a collar orspring-loaded clip on the neck of an animal, or a collar placed aroundthe tail or limb, which can house light emitting diode transmitters andlight receiving sensors to collect photoplesythmography signals. In oneembodiment, the collar or clip includes two pairs of light transmittersand receivers placed at different locations on the neck of the animal inorder to achieve redundancy and improved noise suppression. In anotherembodiment a cage floor having ECG sensors is used to collect ECGsignals from the animal feet. These applications are exemplary of manyapplications characterized by low SNR, for which denoising approaches asdiscussed can be used to render the applications viable for analysis.

In the following examples, performance aspects are discussed as may berelevant to various embodiments illustrated and analyzed, many of whichinvolve the analysis of ECG and PNA signals that can be challenging tocarry out using other approaches. In many of the examples discussedhere, the signal sources are contaminated with noise and the number ofsources is larger than the number of observed signals. Althoughembodiments of the present invention can be used with a broad range ofphysiological signals, exemplary performance is illustrated with ECG andPNA signals that are problematic for traditional signal processingapproaches because of contamination with in-band noise that could not besubstantially reduced without distorting signal morphology. Suchembodiments are applicable to the use of MDSP-based approaches asdiscussed herein, to achieve certain performance-related conditions orcharacteristics, which can be measured or relatively characterized inmanners as discussed herein. Accordingly, various embodiments aredirected to approaches to achieving such things as levels or degrees ofnoise reduction, signal quality, cardiac (or other signal) eventdetection accuracy and more, as facilitated using aspects of theinvention as described herein.

Various MDSP-based embodiments are directed to denoising a physiologicalsignal while preserving the morphology of a desired signal within thephysiological signal (e.g., as recorded). Quality of signalreconstruction (QSR) is a metric commonly employed to assess the abilityof a noise removal technique to preserve morphology of the desiredsignal in signals with moderate to high signal to noise ratio. QSR isdefined as the mean squared error between the original signal x_(cl) anddenoised signal x_(den) calculated sample-by-sample as a percentage ofthe original signal variance.

${QSR} = {100\%*\left( {1 - \frac{\sum\limits_{i}\left( {x_{cl}^{i} - x_{den}^{i}} \right)^{2}}{\sum\limits_{i}\left( x_{cl}^{i} \right)^{2}}} \right)}$

QSR of a denoised signal would be close to 100% if the originalrecording is contaminated with minimal noise and if distortionintroduced into the desired signal by denoising was very small.

FIG. 6 shows plots characterizing an example MDF-based embodimentdirected to removing noise from input 3-lead ECG signals 601,602, and603 from a PhysioNet [32] database. Results of ICA and PCA applieddirectly to the signal are also shown for comparison. The ECG recordingsof FIG. 6 represent signals with portions that are relatively noise free(601) and portions that are noisy (602 and 603), demonstrating theremoval of noise with an MDF-type approach as discussed herein whilealso preserving morphology. In such a short recording, the character ofthe denoising technique, whether MDF, ICA, or PCA, is relativelyconsistent throughout the duration. Evaluating QSR for the portion whichis relatively noise free, therefore, provides an indication of atechnique's ability to preserve morphology while a visual inspection ofthe noisy portion provides for a qualitative assessment of the abilityof a particular technique to suppress noise. For the relatively noisefree portion, the MDF-based approach shows QSR values of 98%, 99%, and92% for the top 610, middle 611, and bottom 612 traces, respectively,indicating that distortion was negligible. The PCA approach shows QSRvalues of 83%, 68%, and 71%, for the top 604, middle 605, and bottom 606traces, respectively, indicating significant distortion of signalcontent. The ICA approach shows QSR values of 2%, 1%, and 4%, for thetop 607, middle 608, and bottom 609 traces, respectively, indicatingvery significant distortion of signal content. Accordingly, variousMDSP-based embodiments are directed to processing signals to addresschallenges relating to such QSR values that may be insufficient, ifprocessed otherwise.

FIG. 7 shows characteristic results of denoising using an MDF-typeapproach (in accordance with an example/experimental-type embodiment),Butterworth bandpass filtering (BPF) with a pass-band of 1 to 60 Hz, andPCA. A relatively noise-free ECG from the PhysioNet Long-Term STdatabase (record s30661) is corrupted with increasing levels ofband-limited (0.05 to 70 Hz) white noise, and processed in accordancewith an example embodiment. The denoising results are quantifiedmeasuring QSR and input and output signal SNR according to the formula:

${SNR}_{d\; B} = {20{\log_{10}\left( \frac{\sigma_{signal}}{\sigma_{noise}} \right)}}$where σ_(signal) and σ_(noise) are respective clean-signal and noisestandard deviations.

Referring to the plots 710 and 711 of FIG. 7, SNR and QSR versus inputsignal SNR achieved by MDF, PCA, and BPF are illustrated. Plot 710illustrates the SNR improvement by comparing input SNR (on x-axis) todenoised signal SNR (on y-axis). Plot 711 illustrates the correspondingQSR for the same range of input SNR. For example, as illustrated in plot710, an input signal with 4 dB SNR is denoised with an MDF-basedapproach, a 9 dB SNR improvement is achieved. Referring to the plot 711,95% of the original clean signal content is preserved following MDFdenoising of an input signal with 4 dB SNR. With increasing input SNR,QSR performance for the MDF-based embodiment quickly approaches 100%with an approximately linear denoising characteristic as measured by SNRon a logarithmic scale. Similar processing with a two-lead ECG generatessimilar results observed for an MDF-based embodiment. As shown in theFIG. 7 the PCA and BPF results can be improved upon using variousembodiments as discussed herein.

Referring to the ECG tracings 707, 708, and 709 of FIG. 7, these threetracings illustrate an input noise-free signal 707; signal 708 corruptedwith band-limited white noise to reduce SNR to 4 dB, and signal 709denoised with an MDF-based approach. Adding band-limited white noise toachieve a 4 dB SNR renders the T and P waves indiscernible, as indicatedin the middle tracing, while application of MDF restores the P and Twaves.

FIG. 8 shows another example embodiment, involving the use of MDF tosuppress noise while preserving morphology for a single-channel ECG.Plot 801 is an input ECG recording corrupted with noise, and plot 802shows the result of applying MDF filtering. The noise present in theinput signal has similar characteristics (frequency content andamplitude) as QRS complexes which would result in QRS detection errorswithout denoising. The application of MDF is used to avoid potential QRSdetection errors, and can remove most of the noise while preservingmorphology of QRS, P, and T waves, allowing for high accuracy detectionof all ECG features, including QRS complex, P, and T-waves.

The embodiments shown in FIGS. 6, 7, and 8 characterize an example useof MDF to suppress in-band noise while substantially preserving signalmorphology, in accordance with various embodiments. This combination ofattributes is useful for a multitude of applications, including clinicaldiagnosis and research involving the measurement and analysis ofphysiological signals. Waveform morphology is preserved for a variety ofapplications, such as for detecting abnormalities in cardiac functionfrom an ECG, evaluating respiratory function from a respiratory signal,evaluating a signal from a photoplethysmography sensor for measuringoxygen saturation, evaluating sleep stages from an EEG, and otherapplications. For example, preserved QRS morphology is used indiagnosing bundle brunch block or ventricular hypertrophy, T-wavemorphology is used in measuring repolarization abnormalities in clinicalcare and drug toxicity studies, and ST segment changes are used whendiagnosing ischemia, electrolyte imbalance, and Brugada syndrome. Inanother example, the ability to preserve P wave and QRS complexmorphology facilitates the analysis of time correspondence of P wave andQRS complex to diagnose AV block.

In another embodiment, an MDSP-based approach is used for denoising andsignal source extraction for monitoring a fetal ECG. In this embodiment,an ECG is recorded by placing sensing leads on the surface of the skinof the mother, typically in the lower abdomen. In one embodiment,individual sources that make up a fetal ECG are separated from theremaining subcomponents in the second domain by applying ICA signalsource extraction techniques in step 2 of the MDSP embodiment. Inanother embodiment the fetal ECG is extracted from the othersubcomponents by spatially selective filtering, periodic componentanalysis, or their combination (e.g., in step 2 as shown in FIGS. 1 and2 above). In this embodiment, the undesired sources, such as maternalECG, are treated as noise and removed, leaving the denoised fetal ECG.

In another embodiment, an MDSP-based technique is used for measuring adegree of synchronization of uterine contractions of a pregnant femalefor predicting and detecting labor. In this embodiment, anelectrohysterogram (EHG) is collected from the female abdomen. In oneembodiment, an MDF-based approach as discussed herein is used to removeECG artifacts from the EHG signal. In one embodiment, the degree ofsynchronization of contractions in denoised EHG signal is measured byamplitude correlation via linear or nonlinear regression and thefrequency relationship measured as coherence, or the amplitude and phasesynchronization in the time-frequency domain. In another embodiment, across wavelet coherence function is used to measure amplitudecorrelation between two contraction bursts [33, 34]. In anotherembodiment, an envelope of a multi-channel EHG signal is calculatedusing a Hilbert transform or low-pass filtering of a rectified EHGsignal to measure amplitude of a contraction wave and its spatialsynchronization.

In other embodiments, a MDSP approach as discussed herein is used fordetecting atrial arrhythmias in an ECG. In these embodiments, atrialactivity can be extracted by applying ICA, SSF, PCA, πCA, or theircombination, as part of step 2 (process 204 and 205) as shown in FIG. 2.In this embodiment, step 2 can involve removing noise as well as thesubcomponents associated with ventricular activity or segments of thesubcomponents as identified by SSF.

In FIG. 9, the results of atrial activity extraction are illustrated onan atrial flutter recording from the PhysioNet database [32]. Plots 901,902, and 903 show ECG recorded from surface leads. Plot 904 is arecording from an intracardiac catheter located near an atrial freewall. The intracardiac recording is shown as an exemplary benchmark ofatrial activity. Plot 905 shows the atrial activity separated from thesurface ECG recordings using an MDSP-based embodiment. Note that thelocations of P-waves on the plot 905 of extracted atrial activitymatches locations of atrial depolarizations recorded from intracardiaclead shown in 904. The gaps in atrial activity coincide with ventriculardepolarizations (i.e., QRS complexes) and repolarizations (T-waves).Analysis of the separated atrial activity can significantly improve theaccuracy of atrial flutter detection.

In FIG. 10, the results of atrial activity extraction are illustrated onan atrial fibrillation recording from the PhysioNet database [32].During atrial fibrillation the P-waves often degenerate into morefractionated and variable f-waves which are barely visible in surfacerecording traces 1001, 1002, and 1003 shown in FIG. 10. Despite that,the atrial activity signal 1005 extracted using an MDSP embodimentclearly shows the f-waves that coincide with the atrial depolarizationtrace 1004 recorded using an intracardiac lead. The only gaps are whenatrial activity coincides with ventricular depolarizations (i.e., QRScomplexes) and repolarizations (T-waves).

FIGS. 9 and 10 demonstrate that various MDSP-based embodiments arecapable of extracting atrial activity from ECG recordings. There are anumber of ways that extracted atrial activity can be used to improve theaccuracy of atrial flutter and fibrillation detection anddiscrimination. For example, spectral analysis of the extracted atrialactivity signal can be used to quantify P-wave regularity and frequencyand discriminate between atrial fibrillation and flutter. In oneembodiment, atrial rate can be estimated by spectral analysis. Inanother embodiment, extracted subcomponents corresponding to atrialactivity can be analyzed in the second domain to estimate the atrialrate. In another embodiment, a P-wave similarity measure is used todetect and discriminate between atrial fibrillation and flutter. Inanother embodiment, zero crossings can be used to estimate atrial ratein the segments where atrial activity is present. The analysis of atrialactivity can be combined with analysis of cardiac cycle variability andregularity as well as PR interval variability to further improve theaccuracy of atrial fibrillation and flutter detection.

In other embodiments an MDSP-based approach is used for separatingventricular depolarization and repolarization activity. This may beuseful for assessing repolarization activity for the risk stratificationof sudden cardiac death. Once repolarization activity is extracted, itcan be used to assess T-wave alternans and morphology, ST elevation inischemia, and other cardiac abnormalities that can be useful forassessing cardiovascular risk.

Another embodiment is directed to using MDF for removing high amplitudenoise from PNA signals. In particular, this is useful when removing highamplitude noise from PNA recordings such as those from the vagal nerve,sympathetic nerves, or motor nerves. In this embodiment, the highamplitude noise or artifact is detected by identifying undesired peaksin subcomponents that do not overlap the band of frequencies associatedwith desired signal. In FIG. 11 exemplary performance of an MDFembodiment is illustrated on a single channel PNA input signal 1101 withhigh amplitude EMG noise. Plot 1101 is a PNA recording from a rat renalnerve while plot 1102 is the denoised signal following application ofMDF. The MDF embodiment extracts the signal source from a very low SNRacquired signal while preserving PNA information. It removes nearly allnoise and artifacts from the recording including myoelectric artifacts(EMG), electrical, and other noises while preserving informationregarding the underlying PNA. An MDF embodiment could be used to extendthe life of preparations where PNA is recorded by improving the abilityto extract accurate information from PNA signals with low SNR. In PNArecording preparations for research in animal models, it is also commonto dose the subject with a drug that shuts down PNA so that baselinenoise can be measured and subsequently subtracted from the neuralsignal. This procedure can be dangerous for the subject and is laborintensive and cumbersome for the researcher. The MDF embodiment removesbackground noise automatically and eliminates the need for such anintervention.

In connection with another example, embodiment, PNA is quantified fromrecordings such as discussed above, via the computation of integratedsympathetic nerve activity. In one embodiment for computing a PNAenvelope, an orthogonal component of the denoised PNA signal is computedusing a transform such as a Hilbert transform, or similar transform. Anenvelope is computed as the square root of the sum of the squareddenoised PNA signal and its orthogonal denoised component. This providesan accurate representation of neural activity without the phase delaysinherent in conventional approaches. The neural activity represented bythe signal envelope has a much lower frequency content compared to theraw PNA signal and thus substantially reduces the bandwidth and samplingrate requirement of a system for measuring PNA activity.

Monitoring devices that transmit raw PNA signals as may be used inaccordance with this or other embodiments, such as that available fromTelemetry Research, Auckland, NZ, employs a sampling rate of about 8,000Hz. By employing this embodiment to calculate an accurate PNA envelope,the sampling rate can be reduced to 100 Hz or less, resulting in areduction of transmitted bandwidth of a factor of 80 and a reduction incurrent drain of a wireless transmitter used to transmit data from anambulatory subject.

In another embodiment, an MDSP-based approach is used for removing noiseand extracting signal sources from signals acquired as a result ofprogrammed periodic stimulation, such as auditory brainstem response andin peripheral nerve stimulation therapies where evoked response isanalyzed to titrate therapy or peripheral nerve recruitment. Thesesignals are often characterized by low SNR and a limited number ofobserved channels. These signals can be segmented in the time domainbased upon knowledge of timing of stimulation. Segmentation in the timedomain allows for the creation of the equivalent of multiple channelsfrom the observed signal, hence increasing the number of dimensions inthe first domain. Following decomposition into the second domain oflarger dimension than the first domain, one or more MDSP-basedembodiments previously described can be applied for denoising and signalsource extraction. For auditory brainstem response, an MDSP-basedembodiment can be used to improve the accuracy of intracranial pressureestimation, such as with a system similar to that described in US PatentApplication Publication 20080200832, and U.S. Pat. No. 6,589,189, whichare fully incorporated herein by reference.

In the case of a peripheral nerve stimulation therapy, evoked responsescan be analyzed to titrate therapy targeted at specific nerve fibers ormonitor neuropathy progression. For example, in vagal nerve stimulationapplied for cardiovascular therapy, the stimulation protocols areoptimized to selectively recruit efferent smaller fibers that controlheart function and block stimulation of efferent larger fibers andafferent fibers that could invoke pain or coughing reflex. Suchapproaches may be implemented in accordance with that described in U.S.Patent Application Publication 20080065158, which is incorporated hereinby reference. A device employed for neural stimulation may incorporate afeedback control of stimulation by observing parameters of an evokedresponse. In this type of neural stimulation device, an MDSP-basedembodiment could be applied to assess evoked response resulting fromnerve stimulation targeted at a specific nerve fiber type. In someimplementations, an MDF-based embodiment is used to achieve an accurateassessment of the response of particular nerve fibers to changes inprogrammed stimulation parameters such as timing, frequency, pulsewidth, pulse repetition, duty cycle and amplitude of stimulation inorder to appropriately affect therapy.

In another embodiment, an MDSP-based embodiment is used to monitorneuropathy progression by measuring changes in nerve conduction velocityof small diameter axons. The changes in these axons can serve as anearlier marker of neuropathy development. An MDSP-based embodiment thatinvolves segmenting the evoked response signal and denoising asdescribed above is used to remove background noise and for measurementof evoked response amplitude and time. In many embodiments, thisapproach is used to facilitate the detection of changes in axons thatcan otherwise be challenging to detect due to low amplitude and phasecancellation of evoked response potentials.

In another embodiment, MDSP is used for detecting feature points of aphysiological signal such as a QRS onset, P-wave onset, or T-wave offsetin an ECG signal or systole and diastole in an arterial blood pressuresignal. Referring to FIG. 12, this feature point detection embodimentuses one or more selected denoised signal subcomponents to compute anemphasis signal that emphasizes a signal wave or feature point ofinterest, computed as a linear or non-linear combination of selectedsubcomponents that are associated with the signal wave of interest. Inanother embodiment, the emphasis signal is computed by performing theinverse of the transform used for decomposing the physiological signalon the selected subcomponents. In some embodiments it may be useful todifferentiate the signal following the inverse transform.

Referring to FIG. 12, subcomponents used to compute the emphasis signalare generated as a result of the decomposition process 1205 of inputsignal 1200 using an MDSP embodiment, as described herein. Thesubcomponents that contain the majority of the energy of the signalwaves are selected for the emphasis signal and are denoised in process1206 using an MDSP embodiment. The emphasis signal computed in process1207 may include frequency subbands matching the spectral energy ofparticular signal waves of interest or a subset of basis functions tunedto the signal wave or feature point of interest and its variationsacross a range of normal, perturbed, and pathological conditions. Inanother embodiment wavelet related subcomponents can be used to computethe emphasis signal in process 1207. The specific subcomponents useddepend upon the decomposition technique used, sampling rate, and thespecies from which the physiologic signal was recorded. Example emphasissignals of an ECG are shown in 1202, 1204, 1211, and 1213 of FIG. 12,along with corresponding ECG waveforms 1201, 1203, 1210, and 1212. Thevertical dashed lines in FIG. 12 show the point of feature pointdetection in each ECG waveform and corresponding emphasis signal.

The transition points of the emphasis signal are evaluated in process1208 to detect feature points, shown by example in 1214 and 1215, of thephysiologic signal to create output 1209 representing the time of thefeature point. In one embodiment, the pattern of significant peaks,valleys, and zero crossings within the emphasis signal are used todetect feature points. In another embodiment, the feature points aredetected by applying a threshold to the emphasis signal. In yet anotherembodiment, the feature points are detected by applying pattern ortemplate matching.

The following illustrative example demonstrates exemplary performance ofan embodiment involving the detection QRS complexes in an ECG. For QRSdetection, an MDSP-based approach is used to compute an emphasis signalfrom a combination of denoised subcomponents that are algorithmicallyselected to include, for example, complexes that are wide, narrow,premature, fractionated, biphasic, monophasic, fibrillatory,tachycardic, or complexes that have been distorted as a result of apharmacological agent.

FIG. 13 provides an illustration of an ECG signal 1301 that is processedin accordance with such an approach, to address problems that may relateto the presence of bigemeny, tall T-waves, and low QRS amplitude, inaddition to rapid changes in QRS amplitude. An emphasis signal computedas described in this MDSP embodiment facilitates the detection of allQRS complexes, as shown by the circles as in 1302 of FIG. 13, with nofalse detection on T-waves. When tested on the MIT BIH Arrhythmiadatabase [35], a QRS detection accuracy of 99.8% sensitivity and 99.8%positive predictive value can be achieved on single lead ECGs. Featuredetection can be enhanced by using and analyzing multiple leadrecordings, using an MDSP-based approach to leverage redundanciesbetween the leads in a manner that is more efficient at removing noise.

In another example embodiment, an MDSP approach is used for extracting arespiration signal from an ECG, blood flow, photoplethysmosgraphy,thoracic impedance, or an arterial blood pressure signal. FIG. 14 showsone such implementation, in which a respiration signal is extractedusing one or more selected denoised signal subcomponents to compute anemphasis signal that is associated with a respiratory pattern. In someembodiments, the subcomponents are filtered with a low-pass filter toextract the low-frequency respiration signal.

In other embodiments, the emphasis signal is combined with a heart ratesignal to improve the accuracy of computed respiratory parameters. Forexample, canines have pronounced respiratory sinus arrhythmia which ischaracterized by heart rate changes that correlate to respiration; thesecharacteristics can be used in connection with these embodiments, forextracting, processing or otherwise using canine respiration signals.

Referring again to FIG. 14, subcomponents resulting from decompositionin process 1402 of input signal 1401 are used to compute an emphasissignal, using an MDSP approach such as described in connection with oneor more example embodiments herein. Subcomponents are denoised inprocess 1403 using an MDSP embodiment as described herein. Subcomponentsthat contain a majority of the energy of the respiration pattern areselected and combined to create an respiratory emphasis signal inprocess 1404, also using an MDSP embodiment as discussed herein. Theemphasis signal may include frequency subbands matching the spectralenergy of particular signal waves of interest or a subset of basisfunctions tuned to the signal wave or feature point of interest and itsvariations across a range of normal, perturbed, and pathologicalconditions. The emphasis signal is processed in 1405 using zerocrossings or spectral analysis to compute respiratory parameters.

In another embodiment (e.g., relative to FIG. 14), wavelet relatedsubcomponents are used to compute an emphasis signal. The specificsubcomponents that are used are selected relative to one or more of thedecomposition technique used, sampling rate, and the species from whichthe physiologic signal was recorded.

In some embodiments, the noise level of an ECG is measured using anembodiment described above, and zero crossings that occur too frequentlyduring noisy segments are discarded (or simply not used). In anotherembodiment, the emphasis signal can be low-pass filtered prior tomeasurement of respiration rate and tidal volume. The tidal volume canbe extracted by measuring peak and valley amplitude between valid zerocrossings. In another embodiment, the tidal volume is computed as afunction of area under the curve between valid zero crossings.

FIG. 15 illustrates another example embodiment as directed to theprocessing of a primate ECG. Plot 1501 is a noisy input ECG, plot 1502is an ECG denoised with a MDF-based approach as described herein, andplot 1503 is an extracted respiration emphasis signal. The residualnoise level is measured and is shown on the bottom plot as a dottedline. This approach is used to assess the validity of the respirationsignal. Zero crossings can be discarded or otherwise not used if thenoise exceeds a predetermined threshold (e.g., as with the sample 23,500in FIG. 15).

An additional MDSP embodiment is directed to detecting events in aphysiological signal by combining aspects of feature point detection andsignal source extraction as discussed herein, to detect cardiacabnormalities and arrhythmias of ventricular and atrial origin, such asventricular fibrillation, tachycardia, bradycardia, atrial fibrillationand flutter, AV block and others. In one embodiment, the intervalsbetween QRS complexes are computed to detect rate abnormalitiesindicative of tachycardia or bradycardia. In another embodiment, atransition to tachycardia and QRS complex morphology are evaluated todiscriminate between sinus-, supra-ventricular or life-threateningventricular tachycardia. In another embodiment, the ventricular rhythmstatistics and separated atrial activity rate are evaluated to detectatrial fibrillation and flutter.

Another embodiment of involving an MDSP-based approach is used toachieve efficient compression of quasi-periodic signals such as ECGsignals, such as by suppressing noise while preserving signal morphologyand providing accurate feature point detection. In one embodiment,arrhythmic events are detected and ECG traces corresponding to theseevents are compressed to reduce the data storage and transmissionbandwidth required to communicate the signal to a location remote fromthe monitored subject. Referring to FIG. 16, an ECG signal sensed byelectrodes 1601 is conditioned by signal conditioning circuits 1602. Thedigitized signal 1609 is decomposed in process 1610 and denoised inprocess 1611 using MDSP techniques described herein. An emphasis signalis computed and QRS complexes detected in process 1612 using an MDSPembodiment described herein. Cardiac events are detected in process 1613using predefined thresholds for heart rate and morphology. The denoisedECG traces of detected arrhythmic events are reconstructed in process1614. The ECG traces are segmented into cardiac cycles and are alignedusing a feature point of the QRS complex in time to form an image inprocess 1615.

The two-dimensional (2D) image plot thus formed includes consecutivecardiac cycles in one dimension and the ECG signal of each cardiac cyclein the other dimension. An illustration of the 2D image is shown as a 3Dplot by way of example in FIG. 17. Plot 1701 shows a 3D plot ofsequential cardiac cycles with cardiac cycle length equalization (e.g.,sequential RR intervals are padded by a constant value at the end of thecycle to ensure that all cardiac cycle lengths are equal). Theredundancy between adjacent beats results in more efficient compression.However, due to physiologic factors such as respiration or rhythmdisturbances the adjacent beat redundancy might be low. In the 3D ECGplot 1702, cardiac cycles are sorted by length (e.g., RR interval),resulting in smoother beat transitions that lead to more efficientcompression. Accordingly, the image can be efficiently compressed byleveraging redundancies between adjacent cardiac cycles. In addition,redundancies across adjacent subbands or wavelet scales can be utilizedby wavelet or cosine transforms of the image. Examples of techniquesused in process 1616 that could be utilized to achieve efficientcompression of the 2D image include transform, subband or wavelet basedencoding techniques such as embedded zerotree wavelet (EZW) [36], setpartitioning in hierarchical trees (SPIHT) [37], modified SPIHT [38] andembedded block coding with optimal truncation (EBCOT) encodingalgorithms [39]. Compression ratios on the order of 15:1 to 20:1 withless than 5% distortion can be achieved using this technique on denoisedECG signals.

In another embodiment, a quasi-periodic signal is compressed by phasewrapping cardiac cycles and converting the source signals into a polaror cylindrical system of coordinates (38). Then the signal can beefficiently represented by a 3 dimensional plot of phase-aligned cardiaccycles and compressed.

In another embodiment a denoised ECG is compressed by computing atemplate QRS complex and subtracting it from detected normal QRScomplexes. The residual signal has lower frequency content and can becompressed by a lossy compression technique that can include subsamplingand quantization. The decompression technique makes use of losslesscoding of the QRS template and QRS complex locations as well as lossycoding of the residual signal to reconstruct the ECG signal with smallamount of distortion.

Other MDSP-based embodiments are used to compress blood pressure, pulseoximetry signals, respiration, heart sounds, and other pseudoperiodicsignals. In order to achieve high levels of compression with any ofthese embodiments without introducing significant signal distortion,accurate QRS or cardiac cycle detection and effective noise suppression(denoising) are used. In some implementations, creating the intermediaterepresentation of the signal that leverages redundancy between thecycles by sorting cycles by length, as in plot 1702 of FIG. 17, is usedto achieve a high compression rate. Accurate cycle detection is used tomitigate the introduction of noise and artifacts into the reconstructedsignal following compression and decompression.

Another embodiment is directed to an MDSP-based approach used tocompress PNA signals and other non-quasi periodic signals, where thedenoising provided by MDSP leads to a sparse signal as demonstrated inplot 1102 of FIG. 11. Note that the denoised signal is almost alwaysnear zero in the absence of a neural spike. The sparse signal iscompressed using one or more compression schemes, such as directtime-domain coding or transform based coding [40]. In general, acompression scheme for a particular physiological signal is selectedbased upon the ability of a given scheme to leverage redundancies in thesignal.

Referring to FIG. 18, another example embodiment is directed to using anMDSP-based approach to evaluate ECG strips captured by an ambulatorymonitoring device 1801 with arrhythmic event detection capability. Thecaptured ECG strips are forwarded to a data review system 1804 andevaluated using an MDSP-based algorithm 1800 implemented on a computingdevice in data review system 1804. Algorithm 1800 is used to evaluatethe captured ECG strips 1805 and assign a classification to each strip.Each ECG strip is assigned one of four classifications using anMDSP-based approach for arrhythmia detection and validity, theclassifications including: A) arrhythmia is present, B) no arrhythmiapresent, C) strip is uninterpretable, or D) uncertain (e.g., the ECGstrip cannot be placed with confidence in classification A, B, or C). Ifan ECG is classified as D, an automatic indicator can be triggered tosuggest human review to determine if an arrhythmia is present. For ECGstrips falling in classifications B and C, no further review is needed.Depending upon the nature of the arrhythmia and the clinical careprocess, ECG strips falling in classification A may be reviewed by aperson to assign a suggested diagnosis prior to forwarding to a decisionmaker. Using this approach and considering a relatively low percentageof the ECG strips evaluated requiring review, labor and costs associatedwith providing review services can be substantially reduced. Inaddition, the quality of review services can be improved, since anaccurate computer-based algorithm can provide better consistency due toelimination of subjectivity.

The system shown in FIG. 18 includes ambulatory monitoring devices 1801,or subject devices, that are worn by patients being evaluated for aheart rhythm disorder or for research. Ambulatory monitoring device 1801includes a computing circuit configured with an algorithm to evaluatethe ECG signal from the patient and, if an arrhythmia is detected,capture an ECG strip containing the arrhythmia in memory. Such capturedECG strips may, for example, be one to five minutes in duration andstored in memory for later wireless communication to a base station 1802(e.g., located in the patient's home), and from the base station 1802 toa data review system 1804 via telecom or data network 1803. The datareview system 1804 may be located at a center where the received ECGstrips can be reviewed, if necessary, to verify the presence of anarrhythmia or to suggest a diagnosis. The information derived from theECGs is then packaged into a report which is forwarded to a researcheror clinician for use in decision making.

The results contained in a report may be provided to physicians,clinics, hospitals, or to organizations engaged in drug safety research,and can be delivered via a service provider system/review center thatprocesses the resulting signals using data review system 1804, possiblyin combination with trained healthcare personnel. Such a review centermay provide services to a large number of clinics and physicians, or itmay be housed within a clinic or a research facility and provide serviceto one or a small number of clinics or research groups or businesses.

In some embodiments, subject devices forward full-disclosure ECGrecordings, and in yet other embodiments ECG strips of 10 second to 5minutes are captured at regular intervals for analysis at the reviewcenter without regard to the content or nature of the ECG signal. Insome embodiments, the subject device from which results are communicatedas above is implanted in a patient. One type of implantable device isthe Reveal XT from Medtronic of Minneapolis, Minn.

Flow chart 1800 in FIG. 18 shows an example embodiment directed toprocessing and evaluation of ECG strips received by data review system1804. A captured ECG strip 1805 received at data review system 1804 isevaluated for the presence or absence of arrhythmias using an MDSP-basedembodiment as described herein. Criteria 1806 input from a care provideror other decision maker is used by process 1807 to determine if anarrhythmia is present. Examples criteria 1806 include a heart rate abovewhich a rhythm is considered to be a tachycardia, a heart rate belowwhich a rhythm is considered to be a bradycardia, and a minimum durationatrial fibrillation (AF) episode required for an occurrence of AF to bereported as an arrhythmia. A dynamic signal-to-noise ratio (dSNR) iscomputed for the ECG strip in process 1808 as described herein. Avalidity metric (VM) may be additionally computed using the dSNR andsignal morphology in process 1808. VM is compared to a ValidityThreshold VT1 in decision point 1809. If VM exceeds VT1, the result isconsidered valid and captured ECG strip 1805 is classified “A” ifprocess 1807 detected an arrhythmia and “B” if process 1807 did notdetect an arrhythmia. If VM does not exceed VT1 at decision point 1809,then VM is compared to a threshold VT2 at decision point 1810 (VT2<VT1).If VM exceeds VT2, then the result is determined to be uncertain and theECG strip is classified as “D” to indicate that review by a personshould be carried out to determine whether an arrhythmia is present inthe ECG strip. If VM does not exceed VT2, then the ECG strip isclassified as uninterpretable (“C”) and is considered uninterpretablebecause the noise level is too high to be evaluated by either human orby automated algorithm.

In some implementations, classifications A, B, and C are assigned with adegree of certainty, designated by the validity metrics VM1 and VM2,sufficiently high that any error in classification can be tolerated bythe user (e.g., 90% likelihood). In some embodiments, the threshold ofcertainty of the classification, VM1 and VM2, is determined by the user.Captured ECGs assigned classification D (uncertain) generally includesegments for which a computing circuit configured with executablesoftware to carry out an MDSP-based processing approach is unable tomake a determination of the rhythm as being classified as A, B, or Cwith a sufficiently high degree of certainty. Segments withclassification D may, for example, contain a level of noise such thatthe denoised signal is not interpretable by the algorithm, but may beinterpretable by a technician, or it may contain an unusual morphologythat the software was unable to recognize.

The embodiments described here for analyzing physiologic signals may beimplemented in a variety of platforms that include a logic circuit orcomputer, with reference made herein to a logic circuit or computingcircuit being applicable to a variety of such circuits operating inaccordance with one or more embodiments discussed herein. In oneembodiment, a microprocessor (such as Pentium or Core microprocessorsavailable from Intel of Santa Clara, Calif.) in a personal computerrunning an operating system such as Windows (available from Microsoft ofSeattle, Wash.) or the Unix standard (set by The Open Group of SanFrancisco, Calif.) is used to execute programming to carry outMDSP-based functions as discussed herein. In another embodiment, amicrocontroller suitable for low-power applications, such as the MSP 430available from Texas Instruments of Dallas, Tex., is implemented tocarry out MDSP-based functions as discussed herein. When a review centeras described above is involved, a microprocessor may be used forsimplicity of implementation and relatively low degree of concern aboutpower consumption. Many implementations involving an MDSP-based approachin an ambulatory monitoring device employ a microcontroller (such as theMSP 430 above) to reduce/minimize power consumption. In otherembodiments where power consumption and size are of high priority,implementation in a silicon-based state machine using a hardwaredescription language such as VHDL may be useful.

In some embodiments, referring to FIGS. 16 and 19, aspects of thepresent invention are implemented using a battery powered orpassively-powered device (e.g., via radio frequency power) that is wornby or implanted within a human or animal subject. Depending upon theapplication and specific design requirements, referring to FIG. 18,various aspects of MDSP-based embodiments discussed herein may bepartitioned between implementation within subject device 1801 andimplementation within the data review system 1804.

Referring to FIG. 19, an apparatus for improving the signal-to-noiseratio of a physiological signal is shown, in accordance with anotherexample embodiment. While referencing an ECG signal, the apparatus shownin FIG. 19 may be implemented with other signals such as blood pressure,respiration, photoplethysmography, blood glucose, blood flow, heartsounds, PNA, EMG, and EEG. In this example embodiment, ECG is sensedusing either surface or implanted electrodes 1901. Signal conditioningcircuits 1902 receives the signal from sensing electrodes 1901 and isconditioned to amplify and filter the signal to remove much of the noiseoutside the bandwidth of the ECG signal. Analog-to-digital conversion(ADC) is accomplished by an ADC on board a Texas Instruments MSP430microcontroller 1903 (shown by way of example, and implementable withother processors).

Referring to the right side of FIG. 19, digitized signal 1906 isprocessed by computer instructions executed by a 16-bit RISC MCU of theMSP430 1903. The digitized signal 1906 is decomposed into a combinationof basis functions (subcomponents) in a second domain of higherdimension than the first domain in process 1907. The dimension of thefirst domain is equal to the number of sensed ECG signals. Decompositionin process 1907 is performed using one of a discrete cosine transform, awavelet related transform, a Karhunen-Loeve transform, a Fouriertransform, a Gabor transform, and a filter bank. A subset ofsubcomponents containing primarily signal energy is identified usingMDSP techniques (e.g., one or more of SSF, PCA, ICA, and πCA) in process1908. In process 1909, a denoised signal is reconstructed from thesubcomponents identified as primarily containing signal energy byperforming the inverse of the transform used to decompose the signal. Itshould be noted that the concepts described here can also be applied toother MDSP-based embodiments beyond denoising, including feature pointdetection, event detection, and computing a dynamic signal-to-noiseratio to evaluate the accuracy of information extracted from aphysiological signal.

Referring back to FIG. 16, another example embodiment involves using anMDSP-based approach for implementing a battery-powered apparatus capableof wirelessly communicating physiological signals. Referring to FIG. 18,this apparatus is one embodiment of subject device 1801. The examplerefers specifically to ECG signals, but a similar embodiment can be usedfor other pseudoperiodic physiological signals such as arterial bloodpressure, respiration, blood oxygen saturation derived from PPG, heartsounds, and blood flow for example. In this example embodiment, ECG issensed using either surface or implanted electrodes 1601. Signalconditioning circuits 1602 receive the signal from electrodes 1601 andamplify and filter the signal to remove much of the noise outside thebandwidth of the ECG signal. Analog-to-digital conversion (ADC) of theconditioned signal is accomplished by an ADC on board a TexasInstruments MSP430 microcontroller 1603.

Referring to the right side of FIG. 16, digitized signal 1609 isprocessed by computer instructions executed by the 16-bit RISC MCU ofMSP430 1603. The MCU is in communication with offboard memory 1605 whichmay be used to provide additional data storage and for storage ofcomputer instructions. The MCU is additionally in communication withwireless transmitter 1604 that can send compressed data to wirelessreceiver 1606 located remote from subject device 1801 of FIG. 18.Wireless receiver 1606 is further in communication with a logic circuitor computer that is configured to execute instructions to decompress thecompressed signal in process 1607. In some embodiments the wirelesstransmitter 1604 and receiver 1606 may each be wireless transceiverscapable of both sending and receiving data. An example of a wirelesstransceiver that may be used in connection with this embodiment is theCC2540 low-energy Bluetooth chip available from Texas Instruments (seeabove).

Referring to the data flow diagram on the right side of FIG. 16,digitized signal 1609 is decomposed in process 1610 into a combinationof basis functions (subcomponents) in a second domain of higherdimension than the first domain. The dimension of the first domain isequal to the number of sensed ECG signals. Decomposition in process 1610is performed using one of a discrete cosine transform, a wavelet relatedtransform, a Karhunen-Loeve transform, a Fourier transform, a Gabortransform, and a filter bank. The subcomponents are denoised in process1611 using MDSP techniques (e.g., one or more of SSF, PCA, ICA, and πCA)and a QRS emphasis signal is computed in process 1612 as a linearcombination of a subset of denoised subcomponents containing QRS signalwave energy. The emphasis signal is evaluated to detect each QRScomplex, as described herein, and a feature signal containing a seriesof feature points indicating the R-R interval of consecutive cardiaccycles is constructed in process 1613. The feature signal and morphologyof the denoised ECG are evaluated in process 1613, as described herein,to detect arrhythmic events, such as bradycardia and tachycardia, andthe denoised ECG strips containing arrhythmic events reconstructed inprocess 1614. The denoised ECG strips to be transmitted are compressedusing processes 1615 and 1616. In process 1615, ECG strips are segmentedby cardiac cycle and adjacent strips are aligned using a feature pointof the QRS complex to form an image of a 3D plot similar to that shownin plot 1701 of FIG. 17. The image is subsequently encoded in process1616 by, for example, applying transform encoding, subband encoding, orwavelet based encoding, after which the encoded image is saved in memoryfor later wireless transmission or it may be sent immediately.

In an alternate embodiment, instead of forming the image by aligningadjacent strips, the strips are arranged by cardiac cycle length, as inplot 1702 of FIG. 17. This results in additional redundancies in stripsadjacent to each other in the image and hence results in more efficientcompression.

In another embodiment a template QRS complex is computed and subtractedfrom detected normal QRS complexes. The residual signal is compressed bya lossy compression technique and the QRS template and QRS complexlocations are compressed using lossless coding.

In order to meet power consumption requirements for implementationwithin a subject device for denoising and wireless communication,computer-executable instructions for carrying one an MDSP-based approachas discussed herein, stored as embedded code within the subject device,are configured to facilitate low-power implementation. In oneembodiment, the computer instructions are optimized using integer orfixed point arithmetic and lifting or B-spline wavelet implementation[41] of a signal decomposition transform in order to reduce and/orminimize the number of clock cycles or machine states required. In suchan embodiment, a portion of the computations required to analyzephysiologic signals may be implemented within the subject device whileothers may be implemented in the data review system. In anotherembodiment, the subject device captures, denoises, and compresses theECG of the subject and information is extracted from the ECG recordingoff-line in the data review system. The data review system may include areview function that facilitates human review of ECGs that wereclassified as uncertain by the algorithm.

Other embodiments are directed to a computer-based system or logiccircuit, such as a computer operating using one or more processorcircuits, which operate using executable modules. Each module (e.g.,software-based module) is executable by a computer circuit to carry outone or more functions or processes as described herein. For instance,one such module may carry out MDSP computations upon input data such asECG data, and transform the data into a denoised output. Thistransformation may involve, for example, the use of a software modulethat, when executed by a computer, carries out the steps as shown inFIGS. 1 and 2. Various other embodiments are directed to similartransformations, which may involve taking and processing one or moreinputs to generate a transformed output useful for one or more of avariety of purposes, such as for detecting physiological conditions.Accordingly, the embodiments discussed herein may be carried out usingsuch a system and/or computer circuit and related executable modules.

In one embodiment, and referring again to FIG. 19, a denoising functionis implemented within a computerized apparatus configured to executeprogramming instructions. A digitized physiological signal 1906 is inputto the computerized apparatus. The device computes a denoised outputsignal from the digitized input signal resulting in an improvement inSNR.

In one implementation, an input physiological signal sensed by ECGelectrodes 1901 is a relatively noise-free single lead ECG recordingfrom a human being or other mammal with a resting heart rate less than150 BPM, contaminated with band-limited (0.5 to 100 Hz) white noise.Following denoising in processes 1907, 1908, and 1909 using an MDSPembodiment, the SNR is improved by at least 5 decibels and the mean QRSamplitude for any 10 second interval of the recording is preservedwithin +/−10% of the mean QRS amplitude of the input signal for the same10 second interval.

In this embodiment, the input ECG can be described as residing in afirst domain having a dimension equal to the number of leads (e.g.,channels) in the recording. For example, a recording consisting of alead set (e.g., Leads I, II, and III), would have three dimensions.Referring to FIG. 19, the digitized input ECG 1906 is decomposed inprocess 1907 into a second domain of higher dimension than the firstdomain. Decomposition into the second domain results in generation ofsubcomponents that represent the information contained in the ECGsignal. The dimension of the second domain is defined as the number ofsubcomponents representing each lead of the ECG multiplied by the numberof leads. In one embodiment, decomposition performed in process 1907 isaccomplished using a transform that largely achieves independence of thesubcomponents. Independence of the subcomponents combined withqualification of the frequency content of each subcomponent facilitatesa more precise identification of the association of a subcomponent orgroup of subcomponents with an aspect (e.g., T-wave) of the ECG signal.

In another embodiment, the subcomponents in the second domain areexamined in process 1908, as described herein, based on their spatialdistribution in time, to identify noise and other aspects of the ECG. Inone embodiment, the spatial distribution is examined using spatiallyselective filtering, whereby aspects or waves of the ECG associated withwider frequency band, such as the QRS complex, are identified andpreserved across the subcomponents. In this embodiment, subcomponentsassociated with high frequencies are preserved for a time windowcorresponding to the QRS complex, but are zeroed out in (or not usedfor) the time window corresponding to the remainder of the cardiaccycle. This allows the morphology of the QRS complex to be preserved,while removing the high frequency noise from the remainder of thecardiac cycle, where the primary signal content corresponds to lowerfrequencies. When the input signal is a multi-lead ECG, principalcomponent analysis and independent component may be used in addition tospatially selective filtering to further enhance the independence ofsubcomponents.

In another example embodiment, referring to FIG. 20, spatially selectivefiltering is used to remove noise from an ECG. FIG. 20a (top) shows acardiac cycle of an ECG waveform showing a P-wave, QRS complex 2001,T-wave, and U-wave, as processed in connection with this embodiment. TheQRS complex is detected and, referring to FIG. 20b (middle), the timespanning the beginning of Q to end of S defines Time Window 2002. Theremainder of the cardiac cycle is defined as Time Window 2003. TimeWindow 2002, containing the QRS complex, includes a wide range offrequencies ranging from low-frequencies to high-frequencies. For theECG of a healthy human, this frequency range may span from 0.5 to 100Hz. In order to preserve the morphology of the QRS complex,subcomponents in this frequency range are preserved and considered to beassociated with a desired signal wave. In one embodiment, the beginningand end of Time Window 2002 are respectively defined by the onset of theQ-wave and the offset of the S-wave. In another embodiment, thebeginning and end of Time Window 2002 are shifted somewhat earlier orlater. Because the signal waves occurring in Time Window 2003 (e.g.,P-wave, T-wave, U-wave) contain lower frequency components, thesubcomponents comprising the low-frequencies, represented by region2005, are preserved and the subcomponents comprising high-frequenciesare removed since they are primarily are associated with noise.

In an alternate embodiment as shown in FIG. 20c , subcomponentsassociated with higher frequencies are removed in Time Windows 2002 and2003 while retaining low-frequency components represented by region2007. Those containing the higher frequencies are then added back duringthe time spanning Time Window 2002, as represented by region 2006, torestore the higher frequency components of the QRS complex. In someimplementations, this approach is carried out to mitigate or avoidmorphology distortion.

In one embodiment, and referring to FIG. 21, an input ECG signal 2101 isdecomposed into subcomponents, as described herein, in process 2102. TheQRS complex is identified in process 2103 using one of a number oftechniques including thresholding, adaptive thresholding, and spatiallyselective filtering, as described herein. Following identification ofthe QRS complex the ECG is segmented by cardiac cycle using the QRScomplex as a fiducial point. In one embodiment, each cardiac cycle ispartitioned into two time windows in process 2104. Referring to FIG. 20,first time window 2002 begins at the onset of the Q-wave of the cardiaccycle and ends at the offset of the S-wave. The second time window 2003begins at the offset of the S-wave and ends at the onset of the Q-waveof the next cardiac cycle. In other embodiments, the start and end timesof windows 2002 and 2003 can differ somewhat from the above relative tothe location of feature points of the QRS complex and some degree ofoverlap in the windows is also acceptable. In other embodiments, thecardiac cycle is partitioned into more than two time windows. Forexample, in an alternate embodiment a first time window includes the QRScomplex, a second time window includes the T-wave, and a third timewindow includes the remainder of the cardiac cycle. The set ofsubcomponents present within time window 2002 are referred to as QRSsuband the set of subcomponents present in time window 2003 are referred toas PTsub.

In process 2105, subcomponents of the set PTsub are evaluated toidentify subcomponent subset PTsubnn that overlaps the spectral contentof (are associated with) a characteristic frequency band, FBpt, of therepresentative ECG signal present in window 2003. The value of asubcomponent represents the energy of the input ECG signal contained ina narrow range of frequencies and the subcomponent is said to beassociated with this narrow range of frequencies.

Process 2105 relies on knowledge of the characteristic frequency band,FBpt, of the desired ECG signal in window 2003. FBpt is pre-identifiedusing a database of representative ECG recordings for a species asdescribed in process 2108.

In process 2106, subcomponents of the set QRSsub are evaluated toidentify subcomponent subset QRSsubnn that overlaps the spectral contentof (are associated with) a characteristic frequency band, FBqrs, of therepresentative ECG signal present in window 2002 of FIG. 20. In oneembodiment, the input ECG signal is preprocessed to remove out-of-bandnoise in a signal conditioning circuit such as 1902 in FIG. 19, by adigital filter implemented in a computing or logic circuit, or acombination thereof. In one embodiment, where the energy of out-of-bandnoise in the input signal is negligible, the energy of the QRSsubcomponents QRSsub and QRSsubnn essentially overlap.

Process 2106 relies on knowledge of the characteristic frequency band,FBqrs, of the desired ECG signal in window 2002. FBqrs is pre-identifiedusing a database of representative ECG recordings for a species asdescribed in process 2108.

The identification of the characteristic frequency bands for time windowis performed by evaluating a database of representative ECG recordingsfor a species (input 2107). The database of representative ECGrecordings is selected to represent a broad scope of ECG morphologies,heart rate, anomalies, noise characteristics, and pathologies. In theprocess 2108, the spectral content of the QRS complex is characterizedand the characteristic frequency band of the QRS complex FBqrs isidentified using spectral analysis techniques. The time window outsideof the QRS complex in cardiac cycle is identified and the characteristicfrequency band of the cardiac cycle outside of the QRS complex, FBpt, isidentified using spectral analysis techniques. It should be noted thatcomputing the characteristic frequency band is typically only performedonce for the ECG sampled for a given species. Once FBpt and FBqrs aredetermined for a given species, they are used as parameters by theprocesses 2105 and 2106 and it is not necessary to recompute them.

In the process 2111 the identified target subcomponents contained insubset PTsubnn and subset QRSsubnn are combined and subjected to theinverse of the transform used in process 2102 to construct a denoisedECG signal.

FIG. 22 shows a system 2200 for computing a denoised ECG signal from aninput signal including a desired ECG signal and noise, according toanother example embodiment. The system includes a logic circuit 2210 anda memory circuit 2212. The memory circuit 2212 stores instructions that,when executed by the logic circuit, carry out the following steps. Forillustration, FIG. 22 represents steps as carried out in the logiccircuit 2210 with various blocks; however, various approaches forcarrying out the steps may be implemented in connection with otherembodiments. Moreover, the logic circuit 2210 may include two or morelogic circuits, such as two or more processors that carry out differentaspects of the respective steps. In addition, the various steps shown inFIG. 22 and described here may be implemented using one or moreembodiments as discussed above, in connection with the other figures andotherwise.

Referring again to FIG. 22, an input ECG signal 2205, in a first domain,is received at a communications input port 2214, and is decomposed atblock 2220 into subcomponents 2225 in a second domain (e.g., of higherdimension than the first domain as discussed hereinabove). QRSwindow-based identification is carried out at block 2230, at which alocation of the QRS complex of a cardiac cycle in the ECG signal isidentified. A first time window in the cardiac cycle that includes theQRS complex is identified, along with at least one time window in thecardiac cycle that does not include the QRS complex. The first timewindow and the at least one time window span the duration of the cardiaccycle.

At block 2240, and for each of the identified time windows, targetsubcomponents 2245 are identified as subcomponents that contain moredesired ECG signal energy than noise energy. For example, when aparticular subcomponent exhibits more energy associated with a desiredECG signal than energy not associated with such a signal, thatsubcomponent may be identified as a target subcomponent. The targetsubcomponents 2245 are used at block 2250 to construct a denoisedphysiological signal 2255.

The denoised ECG signal is then output via a communications output port2216. This output may, for example, involve a wired or wirelesscommunication, and may be carried out in accordance with one or moreembodiments as described herein. In some implementations, some or all ofthe logic circuit 2210 is included as part of an implantable device, andcarries out some or all of the steps in the implantable device, forgenerating the denoised physiological signal 2255. Using this approach,and as consistent with the above, the logic circuit 2210 can beimplemented to significantly reduce the size of the denoised andcompressed physiological signal 2255 (e.g., as relative to approachesthat do not denoise in this manner), and facilitate the communication ofthe denoised and compressed physiological signal using less data and,correspondingly, lower power.

Those skilled in the art will appreciate that various alternative logiccircuits or computing arrangements, including one or more processors anda memory arrangement configured with program code, would be suitable forcarrying out the approaches as discussed herein, including thosediscussed in connection with FIG. 22 above, along with data structuresfor organizing the required data. Such computer code can be encoded in aprocessor executable format and may be stored on and/or provided via avariety of computer-readable storage media or delivery channels such asmagnetic or optical disks or tapes, electronic storage devices, or asapplication services over a network. With specific reference to FIG. 22,the logic circuit 2210 (and memory 2212, where appropriate) may beimplemented with separate components on a circuit board or may beimplemented internally within an integrated circuit. When implementedinternally within an integrated circuit, the logic circuit 2210 can beimplemented as a microcontroller.

The architecture of the logic circuits, processors and computer typecircuits as described herein depends on implementation requirements aswould be recognized by those skilled in the art. In this context, thesecomponents may be one or more general purpose processors, or acombination of one or more general purpose processors and suitableco-processors, or one or more specialized processors (e.g., RISC, CISC,and pipelined).

Referring again to FIG. 22, the memory circuit 2212 may include multiplelevels of cache memory and a main memory, and local and/or remotepersistent storage such as provided by magnetic disks, flash, EPROM, orother non-volatile data storage. The memory circuit 2212 may be read orread/write capable. The logic circuit 2210 may store instructions (e.g.,software) in the memory circuit 2210, read data from and stores data tothe memory circuit 2212, and communicate with external devices throughthe input/output ports 2214 and 2216. These functions may besynchronized by a clock signal generator that may be part of the logiccircuit 2210. The resources of the logic circuit 2210 may be managed byeither an operating system, or a hardware control unit.

Referring to process 1907 in FIG. 19, example transforms that can beused for decomposing the signal into a second domain of higher dimensionwhile enhancing, or maximizing, the independence of the subcomponentsinclude a discrete cosine transform, a wavelet related transform, aKarhunen-Loeve transform, a Fourier transform, a Gabor transform, or afilter bank.

In another embodiment, referring to FIG. 16, a denoising function and adata compression function are implemented within a computerizedapparatus (e.g., a logic circuit) configured to execute programminginstructions. In this embodiment, denoising and compression of an ECGresults in a reduction in bit rate required to retain the information inthe signal. By compressing the ECG signal prior to wirelesstransmission, fewer bits can be used to achieve data transmission, andhence the power consumed in the transmission (e.g., a telemetry link) isreduced. This approach may also be implemented when storing ECG data (orother signal data) in memory, to reduce the data storage space required.

In a more particular example embodiment, a denoising and compressioninvolves reducing the bit rate of a signal by a factor of 15:1 to 20:1,relative to the bit rate of input signal. For instance, such acompression factor can be achieved with a 16-bit digitized single leadECG having a bit rate of 4,000 bits per second and sampled at 250 Hz,and with band-limited (0.5 to 100 Hz) noise corresponding to a SNR of 4dB. The compressed signal is communicated to a receiving device wherethe compressed ECG signal is reconstructed. The denoising approachfacilitates reconstructing QRS complex, from the compressed signal,having mean amplitude for any 10-second interval therein that differsfrom the mean amplitude of the input ECG by 10% or less. In oneembodiment, communication is performed using a wireless link such as aBluetooth transceiver. In some embodiments, events in the ECG, such asarrhythmias, are detected and compression and transmission of the ECG istriggered based on the presence of a detected event.

In this example embodiment, the input ECG is processed by thecomputerized apparatus to first denoise the signal, then detect cardiaccycles, segment the ECG by cardiac cycles and align them in timeaccording to an identifiable fiduciary (e.g., a QRS peak), and form animage consisting of aligned cardiac cycles. The image is then encodedusing a lossy compression technique such as transform encoding, subbandencoding, or wavelet based encoding. The encoded image contains nearlyall of the information in the denoised ECG, but does so with about15-fold fewer bits. The encoded image is reconstructed at a receiverusing the inverse of the transform used to encode the image, and ECGsegments are reconnected to form a denoised version of the input signal.In one embodiment, denoising prior to compression is achieved in thesame manner as described herein for the apparatus used for denoising.

In another embodiment, cardiac cycles are detected by computing a QRSemphasis signal and evaluating peaks, valleys, or slopes to identify theQRS complex. In another embodiment, the QRS complex is detected and atemplate QRS complex that is representative of the average complex issubsequently computed. The template is then subtracted from each QRScomplex, creating a time series consisting of the resulting difference.The time series of residuals is subsequently encoded using a lossycompression technique such as transform encoding, subband encoding, orwavelet based encoding. The template QRS is encoded using a losslesstransform such as Huffman encoding. This approach may be useful, forexample, in connection with an embodiment in which a relatively simplerand less computationally intense compression technique is appropriate(e.g., where QRS morphology is not expected to change rapidly).Accordingly, the computational intensity of denoising and resultingsignal quality/size can be weighed as a tradeoff, for implementation invarious embodiments.

REFERENCES CITED

For general information regarding a variety of fields that may relate toone or more embodiments of the present invention, and for specificinformation regarding the application of one or more such embodiments,reference may be made to the following documents, which are fullyincorporated herein by reference. Various ones of these references arefurther cited above via corresponding numerals, and may be implementedas such.

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Based upon the above discussion and illustrations, those skilled in theart will readily recognize that various modifications and changes may bemade to the present invention without strictly following the exemplaryembodiments and applications illustrated and described herein. Suchmodifications and changes may include, for example, incorporating one ormore aspects described in the above references and/or applying one ormore embodiments thereto, or combining embodiments. These and othermodifications do not depart from the true spirit and scope of thepresent invention, including that set forth in the following claims.

What is claimed is:
 1. A method for computing a denoised ECG signal froman input signal including an ECG signal and noise, the methodcomprising: identifying a location of a QRS complex of a cardiac cyclein the input signal; identifying a first time window in the cardiaccycle that includes the QRS complex; identifying a second time window inthe cardiac cycle that does not include the QRS complex; and removing aband of frequencies from the second time window.
 2. The method of claim1, wherein identifying the second time window includes identifying atleast two time windows that do not include the QRS complex.
 3. Themethod of claim 2, wherein identifying the at least two time windowsthat do not include the QRS complex includes identifying a time windowthat includes a T-wave of the cardiac cycle and another time window thatincludes a portion of the cardiac cycle that does not include theT-wave.
 4. The method of claim 1, wherein identifying the first timewindow includes identifying the first time window having a beginningbased upon an onset of a Q-wave of the cardiac cycle and an ending basedupon an offset of an S-wave of the cardiac cycle, and identifying thesecond time window includes identifying the second time window having abeginning based on the offset of the S-wave and an ending based on theonset of the Q-wave of a next cardiac cycle.
 5. The method of claim 1,wherein the first and second time windows partially overlap one another.6. The method of claim 1, wherein removing the band of frequencies fromthe second time window includes removing a band of frequencies byspatially selective filtering the input signal, and identifying thelocation of the QRS complex of the cardiac cycle in the input signalincludes identifying the QRS locations by spatially selective filteringthe input signal.
 7. The method of claim 1, wherein removing the band offrequencies from the second time window includes removing a band offrequencies corresponding to frequencies outside of a P-wave frequencyrange and a T-wave frequency range for the cardiac cycle, by filteringthe input signal.
 8. The method of claim 7, wherein identifying thesecond time window includes identifying at least two time windows thatdo not include the QRS complex, and filtering the input signal includesapplying different filtering characteristics for filteringrepolarization activity corresponding to the T-wave frequency range andfor filtering atrial activity corresponding to the P-wave frequencyrange.
 9. The method of claim 1, wherein removing the band offrequencies from the second time window includes removing a band offrequencies predominantly associated with noise.
 10. The method of claim1, wherein removing the band of frequencies from the second time windowincludes removing at least two distinct bands of frequencies.
 11. Themethod of claim 1 wherein removing the band of frequencies from thesecond time window includes: decomposing the input signal intosubcomponents; identifying target subcomponents in the second timewindow as subcomponents that contain more energy within a band offrequencies characteristic of a desired ECG signal in the second timewindow than energy outside the band of frequencies characteristic of thedesired ECG signal in the second time window; and removing subcomponentsin the second time window that are not target subcomponents.
 12. Themethod of claim 1, wherein removing the band of frequencies from thesecond time window includes: identifying a band of frequencies to beremoved in the second time window based upon a band of frequenciescharacteristic of the ECG signal in the second time window, computing areference signal based upon locations of the first and second timewindows, and using the reference signal to adjust a transfer function ofan adaptive filter, and thereafter using the adaptive filter to removethe identified band of frequencies from the second time window.
 13. Themethod of claim 1, wherein at least one of identifying the first timewindow and identifying the second time window includes identifying thetime windows based upon pseudoperiodic characteristics of the ECGsignal.
 14. The method of claim 1, wherein removing the band offrequencies includes removing the band of frequencies based uponpseudoperiodic characteristics of the ECG signal.
 15. The method ofclaim 1, wherein at least one of identifying the first time window andidentifying the second time window includes identifying the time windowsbased upon a time-based distribution of components of the input signal.16. The method of claim 1, wherein removing the band of frequenciesincludes removing the band of frequencies based upon a time-baseddistribution of components of the input signal.
 17. The method of claim1, wherein at least one of identifying the first time window andidentifying the second time window includes identifying the time windowsbased upon pseudoperiodic characteristics of the ECG signal and atime-based distribution of components of the input signal.
 18. Themethod of claim 1, wherein removing the band of frequencies includesremoving the band of frequencies based upon pseudoperiodiccharacteristics of the ECG signal and a time-based distribution ofcomponents of the input signal.
 19. The method of claim 1, whereinremoving the band of frequencies includes identifying components of theinput signal that are associated with a desired ECG signal based upon atime-based distribution of the components, and removing a band offrequencies that does not include the components.
 20. The method ofclaim 1, wherein removing the band of frequencies includes modifying theinput signal to generate the denoised ECG signal as an output forprocessing at a computer.
 21. The method of claim 1, further includingpartitioning a cycle of the input signal into the first and second timewindows based upon a characteristic band of frequencies expected for theECG signal within each of the at least two time windows, identifying thelocations of the at least two time windows based upon a location of atleast two feature points of the cycle, and identifying subcomponents inone of the time windows as subcomponents that contain more energy withinthe characteristic band of frequencies, than energy outside thecharacteristic band of frequencies; and wherein removing a band offrequencies from the second time window includes removing the band offrequencies based upon the identified subcomponents.
 22. The method ofclaim 1, wherein removing a band of frequencies from the second timewindow includes removing a band of frequencies having components of theinput signal that have more noise energy than ECG signal energy.
 23. Themethod of claim 1, wherein the input signal has identifiable cycles thatcan be partitioned into time windows, each time window having anassociated band of frequencies, and further including partitioning thecardiac cycle into the first and second time windows, and identifyingthe second time window as a time window including noise subcomponents ofthe input signal as subcomponents that are not associated with afrequency band of a desired ECG signal.
 24. The method of claim 1,wherein removing the band of frequencies includes comparing frequencycontent of subcomponents of the input signal within the at least onetime window, identifying a characteristic band of frequencies viaspectral analysis of a database containing recordings of representativeECG signals, and removing a band of frequencies based upon theidentified characteristic band of frequencies.
 25. The method of claim1, further including decomposing the input signal from a first domaininto subcomponents of the input signal in a second domain, whereinidentifying the first time window includes identifying the first timewindow based upon subcomponents indicative of the QRS complex, andwherein identifying the second time window includes identifying thesecond time window based upon subcomponents indicative of the noise. 26.The method of claim 1, wherein removing the band of frequencies includesremoving the band of frequencies based upon a time-frequencyrepresentation of spectral content distribution that variessynchronously with a feature point of the cardiac cycle.
 27. A methodfor computing an atrial activity signal from an input signal includingan ECG signal and noise, the method comprising: decomposing the inputsignal from a first domain into subcomponents of the input signal in asecond domain; identifying a location of ventricular electrical activityof a cardiac cycle in the ECG signal; identifying a primary time windowin the cardiac cycle that includes ventricular electrical activity;identifying at least one secondary time window in the cardiac cycle thatdoes not include the ventricular electrical activity; identifying targetsubcomponents in the at least one secondary time window that containmore energy within a band of frequencies characteristic of the atrialactivity signal than energy outside the band of frequenciescharacteristic of the atrial activity signal; and constructing theatrial activity signal using at least one of the identified targetsubcomponents.
 28. The method of claim 27, wherein ventricularelectrical activity includes at least one of a QRS complex and a T-wave.29. The method of claim 27, wherein decomposing the input signalincludes using at least one of the following: a discrete cosinetransform; a wavelet related transform; a Karhunen-Loeve transform; aFourier transform; a Gabor transform; and a filter bank.